Music_Recommendation_System_Full_Code.ipynb

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      "source": [
        "# **Music Recommendation System**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h1>Executive summary</h1>**\n",
        "Discovering new songs is a tedious task. Throughout history, music has been a significant part of human life and culture. With the advancement of technology, music from different parts of the world has become more accessible. However, the ever-growing database of music can make the **search and discover experience overwhelming, leading to content overload and straining our cognitive abilities.**\n",
        "\n",
        "The rapid pace of technological evolution has influenced our lives to become more fast-paced and often distracted, driving the demand for solutions that improve efficiency. Music is intertwined with our daily routines, and many industries incorporate music into their business strategies.\n",
        "\n",
        "To overcome content overload and save time, we need a system that provides people with access to personalized music, offering a variety of styles for them to explore. This reduces the burden of decision-making, helps users stay updated with new releases, and allows them to use their time more efficiently. Additionally, music discovery helps people connect with new communities and provides exposure to emerging artists and genres.\n",
        "\n",
        "For these reasons, a music recommendation system serves as an **intelligent assistant** that helps users **discover, enjoy, and stay engaged with music in a way that feels personal, effortless, and exciting.** It allows users to focus on the joy of listening rather than the hassle of searching.\n",
        "\n",
        "With the ever-increasing volume of songs becoming available on the Internet, **searching for songs of interest has become a tedious task in itself** .\n",
        "\n",
        "### **The objective:**\n",
        "\n",
        "Build a recommendation system to propose the top 10 songs for a user based on the likelihood of listening to those songs.\n",
        "\n",
        "\n",
        "**The key questions:**\n",
        "* How can we model user preferences for songs?\n",
        "* How do we measure the similarity between songs and users?\n",
        "* What factors influence a user's likelihood of listening to a song?\n",
        "* How can we account for new or rarely listened-to songs in recommendations?\n",
        "* How do we continuously improve recommendations as user preferences evolve?\n",
        "\n",
        "### **The problem formulation**:\n",
        "\n",
        "We need a way to predict **the top 10 songs that a user is most likely to listen to next, based on their past interactions and collaborative filtering approaches with the platform.** Using data science, we want to solve the problem of making personalized music recommendations by leveraging user data and song features.\n",
        "\n",
        "\n",
        "In this case study we used 6 different algorithms to built the recommendation system, namely:\n",
        "\n",
        "* Popularity-based recommendation system\n",
        "* User-user similarity based collaborative filtering\n",
        "* Item-item similarity based collaborative filtering\n",
        "* Model Based Collaborative Filtering - Matrix Factorization\n",
        "* Cluster-base recommendation system\n",
        "* Content-base recommendation system"
      ],
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        "<h2>Key Insights:</h2>\n",
        "\n",
        "**User-Song Interaction Patterns:**\n",
        "- There are significantly **more users than unique songs**, implying that popular songs are shared among multiple users.\n",
        "- On average, **a user has 37 unique interactions**, suggesting there variety in terms of preferences for users across songs.\n",
        "- **Artist-to-song ratio shows an average of 2-3 songs per artist**, highlighting a potential influence of artist popularity on song interactions.\n",
        "\n",
        "**Interesting Trends in Song Popularity:**\n",
        "- The number of plays is quite low in early years (1924-1950), which could be due to limited music technology.\n",
        "- There was a significant spike in song plays around the early 2000s, coinciding with the rise of digital media platforms(iTunes, MP3 players, etc.) and Y2K.\n",
        "- There is a significant drop in 2011, this might either be because there is a transition toward music streaming services or there is the play data might not have been recorded the same way.\n",
        "\n",
        "**Consolidating all performance:**\n",
        "\n",
        "\n",
        "---\n",
        "| <h3>Technique</h3> | <h3>RMSE</h3> | <h3>Precision</h3> | <h3>Recall</h3> | <h3>F1 Score</h3> |\n",
        "|---------|----|---------|------|---------|\n",
        "|<h4>User-user similarity</h4>|<h4>1.0878|<h4>0.396|<h4>0.629|<h4>0.504|\n",
        "|<h4>User-user Optimized</h4>|<h4>1.0328|<h4>0.408|<h4>0.665|<h4>0.506|\n",
        "|<h4>Item-item similarity</h4>|<h4>1.0394| <h4>0.307|<h4>0.562|<h4>0.397|\n",
        "|<h4>Item-item Optimized</h4>|<h4>1.0328| <h4>0.408|<h4>0.665|<h4>0.506|\n",
        "|<h4>SVD similarity matrix</h4>|<h4>1.0252| <h4>0.41|<h4>0.633| <h4>0.498|\n",
        "|-----------------------------------------------------------------------|--|--|--|--|--|\n",
        "|<h4>SVD Optimized **</h4>|<h4>1.0141| <h4>0.415|<h4>0.635| <h4>0.502|\n",
        "|-----------------------------------------------------------------------|--|--|--|--|--|\n",
        "|<h4>Cluster-based recommendaiton</h4>|<h4>1.0487|<h4> 0.397|<h4>0.582| <h4>0.472|\n",
        "|<h4>Cluster-based Optimized</h4>|<h4>1.0487| <h4>0.397|<h4>0.582| <h4>0.472|\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "Content-based with cosine similarity\n",
        "\n",
        "```\n",
        "[1., 0., 0., ..., 0., 0., 0.],\n",
        "[0., 1., 0., ..., 0., 0., 0.],\n",
        "[0., 0., 1., ..., 0., 0., 0.],\n",
        "       ...,\n",
        "[0., 0., 0., ..., 1., 0., 0.],\n",
        "[0., 0., 0., ..., 0., 1., 0.],\n",
        "[0., 0., 0., ..., 0., 0., 1.]]\n",
        "```\n"
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        "**Comparison of various techniques and their relative performance based on chosen Metric (Measure of success)**:\n",
        "\n",
        "* **Performance**:\n",
        "  - Optimized techniques **gernally perform better** for optimized user-user, optimized item-item, and SVD.\n",
        "  - Cluster-based however has **no improvements** after optimization and the performance metrics remained the same.\n",
        "* **Comparison of each technique**:\n",
        "  - **Optimized user-user and Optimized item-item perform similarly**, both showing moderate improvements in terms of RMSE, precision, recall, and F1 scores.\n",
        "  - **Optimized SVD has the best overall performance** with the lowest RMSE of 1.0141 and balanced precision/recall.\n",
        "  - **Cluster-based filtering has the worst performance**, with a higher RMSE of 1.0487 and lower precision and recall than all the other methods.\n",
        "  - The matrix for **cosine similarity for content-based filtering** is very sparse (many 0s), which indicates the model struggles to find similarities between different songs, leading to low diversity in recommendation.\n",
        "  - **Content-based filtering** struggled due to sparse cosine similarity matrices. This indicate that there is a lack of diversity in recommendations as it relied heavily on limited features.\n",
        "\n",
        "* **Other relevant insights:**\n",
        "  - The **trade-off between precision and recall:** across all models, there is a trade-off between precision and recall. Models like **SVD** and **user-user similarity** have relatively high recall but their precision is moderate.\n",
        "  - **Content-based filtering needs more diverse and richer features:** the recommendations from the content-based filtering method are dominated by a small set of highly similar items like artists. The **sparse cosine similarity matrix** indicates that the model relies too heavilty on limited textual features, leading to a repetitive and less personalized recommendations.  \n",
        "  - **Optimized SVD is the best performing model:** the model has the **lowest RMSE** (1.0141), indicating that it predicts user ratings with the highest accuracy. The balance between precision(0.415) and recall(0.635) make it the best model in terms of **prediction accuracy and recommendation relevance**.\n",
        "- **Areas of improvements:** The content-based filtering system can try to incorporate richer textual data - expanding content features by adding more detail metadata like genres, tags or use n-grams to improve overlap between items. Based on our initial question, these factors might influence the likelihood of our users' preferences.\n",
        "  - We can consider combining content-based filtering with collaborative filtering such as SVD to capture both content and user preferences.\n",
        "  - Fine tune the **optimized SVD** algorithm further by experimenting with different hyperparameters to improve the precision and recall.\n",
        "  - Consider **tuning the Threshold and k-values** to find optimal balance between precision and recall.\n",
        "\n",
        "**<h3>Proposal for the final solution design:</h3>**\n",
        "- Hybrid Recommendation System (Collaborative filtering (SVD) + Content-based filtering)\n",
        "  - Combining strengths of both approaches can generate a more **personalized recommendation** based on user behavior and interaction, while content-based filtering can **boost item diversity** and recommend new or less-interacted songs by analyzing their features.\n",
        "  - **Improving diversity and relevance:** collaborative filtering alone can sometimes lead to overfitting and overly narrow recommendations. Content-based filtering alone can suffer from lack of diversity. The hybrid approach can increase diversity of recommendations and use collaborative filtering to ensure relevance of the recommendation.\n",
        "  \n",
        "* **Implementation Suggestions:**\n",
        "  1. Apply **Collaborative filtering (SVD)** to generate top-k set of\n",
        "recommendations based on user-item interactions.\n",
        "  2. For items where collaborative filtering lacks data (new songs or sparse interactions) use **content-based filtering to recommend songs** that share key features based on metadata, audio features, or textual content.\n",
        "  3. **Combine results** by either blending the scores or by offering a mixed list of recommendations that include both personalized and content-similar songs."
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        "**<h1>Problem and Solution Summary</h1>**\n",
        "\n",
        "**Problem with the current solution:**\n",
        "* The current solution relies on individual recommendation techniques, which even the best performing algorithm (Optimized SVD) **still has obtains a relatively low accurracy (RSME: 1.0141)**. Although the recall is acceptable (0.635), there are still 36.5% relevant songs that are not recommended to users. There is still a substantial portion of songs missing that are relevant and that users might enjoy.\n",
        "* There is a **lack of diversity** in the current recommendation models, making the recommendation repetitive and lacks variety.\n",
        "* SVD could face **computational challenges** in processing and updating recommendations in large-scale systems.\n",
        "\n",
        "\n",
        "\n",
        "**What does the proposed solution bring?**\n",
        "\n",
        "The **hybrid recommendation system** combining **SVD-based collaborative filtering** and **content-based filterin**g is proposed to improve 3 aspects of the recommendation system:\n",
        "\n",
        "1. **Improve personalization**\n",
        " * **SVD** uses user interaction data (such as play counts) to predict which song a user will likely enjoy. It ensures that the recommendation are personalized based on collective behavior and similar users.\n",
        "\n",
        "2. **Enhance Diversity:**\n",
        " * **Content-based filtering** analyzes song metadata such as genre, artist, year, and more, to recommend songs with similar features. This will help recommending new or rarely interacted songs, addressing the cold start problem where collaborative filtering along struggles to recommend content with our sufficient user interaction data.\n",
        "3. **Balance between Relevance and Variety:**\n",
        " * The **hybrid system** aims to provide a balance between relevant suggestions based on past user behaviors and introducing new/diverse suggestions. This ensures that users are not stuck with repetive recommendations, enhancing the experience of listeners.  \n",
        "\n",
        "**Business Impact**\n",
        "\n",
        "1. **Increase User Engagement:**\n",
        " - By delivering more personalized and diverse recommendations, it encourages users to spend more time on the platform. This can increase user satisfaction and retention.  \n",
        "2. **Potential Revenue Growth:**\n",
        " - Improved recommendations mean users are more likely to remain on the platform for longer periods, which can link to **increase in revenue via subscription and advertisements**. More listening hours can result in better monetizing opportunities.\n",
        "3. **Enhanve Discovery of New Content:**\n",
        "- The system helps user discover new songs and artists, which is crucial for long-term engagement, especially as the music library expands.\n",
        "4. **Better Artist Exposure:**\n",
        " - Using content-based filtering to **expose less popular songs or new artists** to a wider audience can help the platform support emerging talent while catering to diverse music tastes.\n",
        "\n",
        " In summary, as the song library expands, the hybrid recommendation system will solve problem of **content overload** by enhancing relevance, diversity, and personalized recommendations. This leads to an improved user-engagement and business growth."
      ],
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        "**<h1>Key Recommendation to Implement the Solutions:</h1>**\n",
        "\n",
        "**Implementation Suggestions:**\n",
        "1. **Combined SVD (Collaborative Filtering) with Content-based filtering** to create a system that leverages user-item interactions while incorporating song metadata.\n",
        "2. **Improved Data Enrichment** by expanding song features beyond just metadata and incorporate richer textual and audio data like song lyrics, user-generated tags, or acoustic features.\n",
        "3. **Personalized Thresholds and Hyperparameters for Users:** Fine-tune thresholds for the number of recommendations, similarity metrics, and hyperparameters (like k-values) in SVD to better balance the precision recall for different user segments - heavy listeners, casual listeners...\n",
        "4. **Improve Diversity:** Take the **diversity constraint** into account to prevent over-repetition of similar songs and encourage more diverse recommendations.\n",
        "5. **Iterate on A/B Testing and Evaluation:** Implement a continuous evalution pipeline that measures model performance and user engagment through A/B testing using different recommendation strategies.\n",
        "\n",
        "**Potential Benefits and Costs**\n",
        "\n",
        "**Benefits**\n",
        "- **Increase User Engagement and Satisfaction:** when songs are more personalized and diverse it is likely that users will spend more time on the platform because they are satisfied with the recommendation.  \n",
        "- **Revenue Growth:** high engagement leads to high impression, more subscription, and higher overall monetization.\n",
        "- **Artist Exposure:** our algorithm tries to diversify the suggestions that are relevant and can provide better exposure for new artists to more audiences.\n",
        "\n",
        "**Costs**\n",
        "- **Engineering and Development:** Building a hybrid system and data enrichment requires signification Engineering resources.\n",
        "- **Computational Power:** Running the SVD algorithm coupled with personalized algorithms at scale will require more storage and computational power and/or high capacity infrastructure.\n",
        "- **Maintenance:** The model requires iterative fine-tuning, testing, and evaluation to adapt to the users' evolving behavior.\n",
        "\n",
        "**Key Actionables for Stakeholders:**\n",
        "1. **Product Manager,  UX, and Engineering Teams**\n",
        " - Understand the goal/purpose/vision (Product Managers)\n",
        " - UXer designs user interface, create research protocols, Qualitative research for user validation and create a prototype to test the proposed model.\n",
        " - Engineers Implement the API and Frontend for the platform, system design and infrastructure design that is prepared for scale and high availability.\n",
        "2. **Data Science Teams:**\n",
        " - Create a database of user data with more personalized content.\n",
        " - Develop algorithms for personalized threshold and diversity constraints, evaluate, and optimize the recommendation iteratively.\n",
        "3. **Business and Strategy Teams:**\n",
        " - Align the recommendation improvements with business goals, user retention, engagement, and revenue.\n",
        "4. **Users of the System:**\n",
        "  - User is the main stakeholder who will be testing and validating that our recommendation is working.\n",
        "\n",
        "**Risk and Challenges**\n",
        "1. **Scalability Issues:** as the system grows, managing large datasets that requires high computational power will require a highly scalable infrastructure. Increasing computational power and storage requirements can pose as potential bottlenecks.\n",
        "2. **Model Complexity:** the hybrid system can be difficult to maintain and optimize.\n",
        "3. **Overfitting and Underfitting:** Fine-tuning models like SVD can lead to overfitting or underfitting, it requires careful monitoring.\n",
        "4. **User Privacy:** when a recommendation becomes more personalized, there may be concerns over data used to model user preferences such as brower cookies, emails, or ad impression. Ensuring privacy and compliance with data regulation is critical.\n",
        "\n",
        "**Further Analysis Needed/Associated Problems to Solve:**\n",
        "1. **Long-Term User Behavioral Analysis:** Understanding users over time is crucial to improving the recommendation system. Additional analysis of periodical pattern in user interactions may help refine the models.\n",
        "2. **Leveraging Social and Behavorial Data:** Analyzing how users interact with other users suchs followed playlists, sharing songs, and other actions, can be an additional factor to consdier for the recommendation system to provide more socially relevant suggestions.\n"
      ],
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      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
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        "## **Data Dictionary**\n",
        "\n",
        "The core data is the Taste Profile Subset released by the Echo Nest as part of the Million Song Dataset. There are two files in this dataset. The first file contains the details about the song id, titles, release, artist name, and the year of release. The second file contains the user id, song id, and the play count of users.\n",
        "\n",
        "**song_data**\n",
        "\n",
        "- song_id - A unique id given to every song\n",
        "- title - Title of the song\n",
        "- Release - Name of the released album\n",
        "- Artist_name - Name of the artist\n",
        "- year - Year of release\n",
        "\n",
        "**count_data**\n",
        "\n",
        "- user _id - A unique id given to the user\n",
        "- song_id - A unique id given to the song\n",
        "- play_count - Number of times the song was played\n",
        "\n",
        "## **Data Source**\n",
        "http://millionsongdataset.com/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "### **Importing Libraries and the Dataset**"
      ]
    },
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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# Mounting the drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R4YvKrpzId3K"
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      "source": [
        "# Used to ignore the warning given as output of the code\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Basic libraries of python for numeric and dataframe computations\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Visualization libraries\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "# Import the required library to compute the cosine similarity between two vectors\n",
        "import sklearn\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "# Import the required library to split data into train and test datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Import defaultdict from collections A dictionary output that does not raise a key error\n",
        "import collections\n",
        "from collections import defaultdict\n",
        "\n",
        "# Impoort mean_squared_error : a performance metrics in sklearn\n",
        "import sklearn.metrics as metrics\n",
        "from sklearn.metrics import mean_squared_error"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bUGKX140wf-S"
      },
      "source": [
        "### **Load the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "si6ulhIYImck"
      },
      "outputs": [],
      "source": [
        "# Importing the datasets\n",
        "song_df = pd.read_csv('/content/drive/MyDrive/MIT_ADS/Capstone/song_data.csv')\n",
        "count_df = pd.read_csv('/content/drive/MyDrive/MIT_ADS/Capstone/count_data.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "12TKB2M7XyC6"
      },
      "source": [
        "### **Understanding the data by viewing a few observations**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "height": 363
        },
        "outputId": "faa02699-a143-42aa-f427-b0dac3092049"
      },
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              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOBXHDL12A81C204C0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOBYHAJ12A6701BF1D</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SODACBL12A8C13C273</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SODDNQT12A6D4F5F7E</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SODXRTY12AB0180F3B</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOFGUAY12AB017B0A8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOFRQTD12A81C233C0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOHQWYZ12A6D4FA701</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9ff8aef0-db0f-492e-839c-654a7a9fd22d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9ff8aef0-db0f-492e-839c-654a7a9fd22d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9ff8aef0-db0f-492e-839c-654a7a9fd22d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0906ea83-09c3-4d56-89e2-c9776be64e11\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0906ea83-09c3-4d56-89e2-c9776be64e11')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0906ea83-09c3-4d56-89e2-c9776be64e11 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "count_df"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Display first 10 records of count_df data\n",
        "count_df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tV1ed0ApXpu3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "575a34d5-583b-446b-886d-6551020515dc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              song_id                                              title  \\\n",
              "0  SOQMMHC12AB0180CB8                                       Silent Night   \n",
              "1  SOVFVAK12A8C1350D9                                        Tanssi vaan   \n",
              "2  SOGTUKN12AB017F4F1                                  No One Could Ever   \n",
              "3  SOBNYVR12A8C13558C                                      Si Vos Querés   \n",
              "4  SOHSBXH12A8C13B0DF                                   Tangle Of Aspens   \n",
              "5  SOZVAPQ12A8C13B63C  Symphony No. 1 G minor \"Sinfonie Serieuse\"/All...   \n",
              "6  SOQVRHI12A6D4FB2D7                                   We Have Got Love   \n",
              "7  SOEYRFT12AB018936C                                  2 Da Beat Ch'yall   \n",
              "8  SOPMIYT12A6D4F851E                                            Goodbye   \n",
              "9  SOJCFMH12A8C13B0C2                         Mama_ mama can't you see ?   \n",
              "\n",
              "                                release  \\\n",
              "0                 Monster Ballads X-Mas   \n",
              "1                           Karkuteillä   \n",
              "2                                Butter   \n",
              "3                               De Culo   \n",
              "4  Rene Ablaze Presents Winter Sessions   \n",
              "5      Berwald: Symphonies Nos. 1/2/3/4   \n",
              "6             Strictly The Best Vol. 34   \n",
              "7                               Da Bomb   \n",
              "8                             Danny Boy   \n",
              "9  March to cadence with the US marines   \n",
              "\n",
              "                                      artist_name  year  \n",
              "0                                Faster Pussy cat  2003  \n",
              "1                                Karkkiautomaatti  1995  \n",
              "2                                  Hudson Mohawke  2006  \n",
              "3                                     Yerba Brava  2003  \n",
              "4                                      Der Mystic     0  \n",
              "5                                David Montgomery     0  \n",
              "6                              Sasha / Turbulence     0  \n",
              "7                                      Kris Kross  1993  \n",
              "8                                    Joseph Locke     0  \n",
              "9  The Sun Harbor's Chorus-Documentary Recordings     0  "
            ],
            "text/html": [
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>song_id</th>\n",
              "      <th>title</th>\n",
              "      <th>release</th>\n",
              "      <th>artist_name</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SOQMMHC12AB0180CB8</td>\n",
              "      <td>Silent Night</td>\n",
              "      <td>Monster Ballads X-Mas</td>\n",
              "      <td>Faster Pussy cat</td>\n",
              "      <td>2003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SOVFVAK12A8C1350D9</td>\n",
              "      <td>Tanssi vaan</td>\n",
              "      <td>Karkuteillä</td>\n",
              "      <td>Karkkiautomaatti</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SOGTUKN12AB017F4F1</td>\n",
              "      <td>No One Could Ever</td>\n",
              "      <td>Butter</td>\n",
              "      <td>Hudson Mohawke</td>\n",
              "      <td>2006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SOBNYVR12A8C13558C</td>\n",
              "      <td>Si Vos Querés</td>\n",
              "      <td>De Culo</td>\n",
              "      <td>Yerba Brava</td>\n",
              "      <td>2003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SOHSBXH12A8C13B0DF</td>\n",
              "      <td>Tangle Of Aspens</td>\n",
              "      <td>Rene Ablaze Presents Winter Sessions</td>\n",
              "      <td>Der Mystic</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>SOZVAPQ12A8C13B63C</td>\n",
              "      <td>Symphony No. 1 G minor \"Sinfonie Serieuse\"/All...</td>\n",
              "      <td>Berwald: Symphonies Nos. 1/2/3/4</td>\n",
              "      <td>David Montgomery</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>SOQVRHI12A6D4FB2D7</td>\n",
              "      <td>We Have Got Love</td>\n",
              "      <td>Strictly The Best Vol. 34</td>\n",
              "      <td>Sasha / Turbulence</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>SOEYRFT12AB018936C</td>\n",
              "      <td>2 Da Beat Ch'yall</td>\n",
              "      <td>Da Bomb</td>\n",
              "      <td>Kris Kross</td>\n",
              "      <td>1993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>SOPMIYT12A6D4F851E</td>\n",
              "      <td>Goodbye</td>\n",
              "      <td>Danny Boy</td>\n",
              "      <td>Joseph Locke</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>SOJCFMH12A8C13B0C2</td>\n",
              "      <td>Mama_ mama can't you see ?</td>\n",
              "      <td>March to cadence with the US marines</td>\n",
              "      <td>The Sun Harbor's Chorus-Documentary Recordings</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  </svg>\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        document.querySelector('#df-6459a2ce-dc35-4064-ab25-47d6d12aecb4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6459a2ce-dc35-4064-ab25-47d6d12aecb4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "song_df"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Display first 10 records of song_df data\n",
        "song_df.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bvKb5FHcXzcN"
      },
      "source": [
        "### **Let us check the data types and and missing values of each column**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yyoHc_cnX19J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ae212b8-99ac-4f0b-ceff-12d732d3fa53"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2000000 entries, 0 to 1999999\n",
            "Data columns (total 4 columns):\n",
            " #   Column      Dtype \n",
            "---  ------      ----- \n",
            " 0   Unnamed: 0  int64 \n",
            " 1   user_id     object\n",
            " 2   song_id     object\n",
            " 3   play_count  int64 \n",
            "dtypes: int64(2), object(2)\n",
            "memory usage: 61.0+ MB\n",
            "\n",
            "The shape of this dataframe(2000000, 4)\n"
          ]
        }
      ],
      "source": [
        "# Display info of count_df\n",
        "count_df.info()\n",
        "print(f\"\\nThe shape of this dataframe{count_df.shape}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for missing values in the count_df\n",
        "\n",
        "print(count_df.isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kDK58sYXyV-S",
        "outputId": "e67b3680-d493-4a09-ac83-8659df022eaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unnamed: 0    0\n",
            "user_id       0\n",
            "song_id       0\n",
            "play_count    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rz3zDx_LX42y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2349b68e-c334-4cc3-b94a-8595f579e234"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000000 entries, 0 to 999999\n",
            "Data columns (total 5 columns):\n",
            " #   Column       Non-Null Count    Dtype \n",
            "---  ------       --------------    ----- \n",
            " 0   song_id      1000000 non-null  object\n",
            " 1   title        999983 non-null   object\n",
            " 2   release      999993 non-null   object\n",
            " 3   artist_name  1000000 non-null  object\n",
            " 4   year         1000000 non-null  int64 \n",
            "dtypes: int64(1), object(4)\n",
            "memory usage: 38.1+ MB\n",
            "\n",
            "The shape of this dataframe(1000000, 5)\n"
          ]
        }
      ],
      "source": [
        "# Display info of song_df\n",
        "song_df.info()\n",
        "print(f\"\\nThe shape of this dataframe{song_df.shape}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for missing values in the count_df\n",
        "\n",
        "print(song_df.isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nQO7g87iyZHe",
        "outputId": "38e8425a-bb5e-4259-d6a4-eb510dae9e64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "song_id         0\n",
            "title          17\n",
            "release         7\n",
            "artist_name     0\n",
            "year            0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's check to see how many duplicates there are in the song_df\n",
        "\n",
        "song_df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WjGlRu7oyl8_",
        "outputId": "8f04bf42-6672-4905-9a68-5c091aa62d31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "498"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ze2TlWxpYadn"
      },
      "source": [
        "#### **Observations and Insights:**\n",
        "* There are no duplicates and no missing values in the **Count** dataset.\n",
        "* There are 498 duplicates in **song** dataset and **24 missing** values.\n",
        "* We should drop the duplicates from this dataset."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dropping the missing value rows from the dataset\n",
        "\n",
        "song_df = song_df.dropna()"
      ],
      "metadata": {
        "id": "h_d3cOS8yvKQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking all columns after dropping the missing values\n",
        "\n",
        "song_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W2wLojzdy46O",
        "outputId": "ee979545-5f37-42ab-ef41-3bcb57afe434"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 999976 entries, 0 to 999999\n",
            "Data columns (total 5 columns):\n",
            " #   Column       Non-Null Count   Dtype \n",
            "---  ------       --------------   ----- \n",
            " 0   song_id      999976 non-null  object\n",
            " 1   title        999976 non-null  object\n",
            " 2   release      999976 non-null  object\n",
            " 3   artist_name  999976 non-null  object\n",
            " 4   year         999976 non-null  int64 \n",
            "dtypes: int64(1), object(4)\n",
            "memory usage: 45.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking there are no longer any missing values\n",
        "\n",
        "song_df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "me9Wd4HdzxWW",
        "outputId": "1e422276-c0df-40f0-8036-bfba05301b98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "song_id        0\n",
              "title          0\n",
              "release        0\n",
              "artist_name    0\n",
              "year           0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>song_id</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>release</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>artist_name</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oTeurvID2T9U"
      },
      "outputs": [],
      "source": [
        "# Left merge count_df and song_df on \"song_id\". Drop duplicates from song_df data simultaneously, we use inner join because we want to keep only matching rows.\n",
        "song_with_count = count_df.merge(song_df.drop_duplicates(['song_id']), on='song_id', how='left')\n",
        "\n",
        "# Drop the column 'Unnamed: 0'\n",
        "song_with_count.drop('Unnamed: 0', axis=1, inplace=True)\n",
        "\n",
        "## Name the obtained dataframe as \"df\"\n",
        "df = song_with_count"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yWeY9ZT43XFX"
      },
      "source": [
        "**Think About It:** As the user_id and song_id are encrypted. Can they be encoded to numeric features?"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n"
      ],
      "metadata": {
        "id": "EVz34C3rlyEX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oxeoOVxh2T9U"
      },
      "outputs": [],
      "source": [
        "# Apply label encoding for \"user_id\" and \"song_id\"\n",
        "user_encoding = LabelEncoder()\n",
        "song_encoding = LabelEncoder()\n",
        "\n",
        "df['user_id'] = user_encoding.fit_transform(df['user_id'])\n",
        "df['song_id'] = song_encoding.fit_transform(df['song_id'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Q9EFYwj35Ju"
      },
      "source": [
        "**Think About It:** As the data also contains users who have listened to very few songs and vice versa, is it required to filter the data so that it contains users who have listened to a good count of songs and vice versa?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gcY5LKAQvk9J"
      },
      "source": [
        "A dataset of size 2000000 rows x 7 columns can be quite large and may require a lot of computing resources to process. This can lead to long processing times and can make it difficult to train and evaluate your model efficiently.\n",
        "In order to address this issue, it may be necessary to trim down your dataset to a more manageable size."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Count the total number of songs a user listens to.**"
      ],
      "metadata": {
        "id": "GgLlBJh9mXka"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7GGH9TW0_9uX"
      },
      "outputs": [],
      "source": [
        "# Get the column containing the users\n",
        "users = df.user_id\n",
        "\n",
        "# Create a dictionary that maps users(listeners) to the number of songs that they have listened to\n",
        "playing_count = dict()\n",
        "\n",
        "for user in users:\n",
        "    # If we already have the user, just add 1 to their playing count\n",
        "    if user in playing_count:\n",
        "        playing_count[user] += 1\n",
        "\n",
        "    # Otherwise, set their playing count to 1\n",
        "    else:\n",
        "        playing_count[user] = 1"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from itertools import islice # import the islice object\n",
        "\n",
        "# Get the first 10 items from the dictionary\n",
        "first_10_items = dict(islice(playing_count.items(), 10))\n",
        "\n",
        "# Print the first 10 items\n",
        "print(first_10_items)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GW5XmcGVmll-",
        "outputId": "b1d0faaa-7ce9-4a67-ef4f-954ad4cd8951"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{54961: 45, 39877: 1, 56522: 8, 40874: 2, 45012: 19, 22570: 13, 66678: 25, 47087: 11, 46575: 6, 54498: 66}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Cut off at 90 songs - Filter out users who have listened to fewer than 90 songs**"
      ],
      "metadata": {
        "id": "d6WmosnomehJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-cc6mOK7_9uX"
      },
      "outputs": [],
      "source": [
        "# We want our users to have listened at least 90 songs\n",
        "SONG_COUNT_CUTOFF = 90\n",
        "\n",
        "# Create a list of users who need to be removed\n",
        "remove_users = []\n",
        "\n",
        "for user, num_songs in playing_count.items():\n",
        "\n",
        "    if num_songs < SONG_COUNT_CUTOFF:\n",
        "        remove_users.append(user)\n",
        "\n",
        "df = df.loc[ ~ df.user_id.isin(remove_users)]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Count how many unique users ahve listened to each song**"
      ],
      "metadata": {
        "id": "s_lQp9BZnmR6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B5BS-Wk5_9uY"
      },
      "outputs": [],
      "source": [
        "# Get the column containing the songs\n",
        "songs = df.song_id\n",
        "\n",
        "# Create a dictionary that maps songs to its number of users(listeners)\n",
        "playing_count = dict()\n",
        "\n",
        "for song in songs:\n",
        "    # If we already have the song, just add 1 to their playing count\n",
        "    if song in playing_count:\n",
        "        playing_count[song] += 1\n",
        "\n",
        "    # Otherwise, set their playing count to 1\n",
        "    else:\n",
        "        playing_count[song] = 1"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Cut off at 120 - Filter out users who listens to less than 120 songs**"
      ],
      "metadata": {
        "id": "a0-zg0kknus8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_nCtGwGO_9uY"
      },
      "outputs": [],
      "source": [
        "# We want our song to be listened by atleast 120 users to be considred\n",
        "LISTENER_COUNT_CUTOFF = 120\n",
        "\n",
        "remove_songs = []\n",
        "\n",
        "for song, num_users in playing_count.items():\n",
        "    if num_users < LISTENER_COUNT_CUTOFF:\n",
        "        remove_songs.append(song)\n",
        "\n",
        "df_final= df.loc[ ~ df.song_id.isin(remove_songs)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Os9Pmd_Mhuhb"
      },
      "source": [
        "Out of all the songs available, songs with play_count less than or equal to 5 are in almost 90% abundance. So for building the recommendation system let us consider only those songs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8qaKeoMcGpad"
      },
      "outputs": [],
      "source": [
        "# Keep only records of songs with play_count less than or equal to (<=) 5\n",
        "df_final = df_final[df_final.play_count<=5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aL1JZ00o5JtQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a8a0433-7e62-40b9-89a8-ee4f845dddaf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(117876, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "# Check the shape of the data\n",
        "df_final.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_final.head(15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "LtzUev72tAgm",
        "outputId": "8da06a3f-c9c3-497d-b1d8-20bf1bc33862"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     user_id  song_id  play_count                            title  \\\n",
              "200     6958      447           1               Daisy And Prudence   \n",
              "202     6958      512           1  The Ballad of Michael Valentine   \n",
              "203     6958      549           1        I Stand Corrected (Album)   \n",
              "204     6958      703           1            They Might Follow You   \n",
              "205     6958      719           1                       Monkey Man   \n",
              "206     6958      892           1                  Bleeding Hearts   \n",
              "209     6958     1050           5                      Wet Blanket   \n",
              "213     6958     1480           1                    Fast As I Can   \n",
              "215     6958     1671           2              Sleeping In (Album)   \n",
              "216     6958     1752           1                   Gimme Sympathy   \n",
              "217     6958     1756           1       You Mustn't Kick It Around   \n",
              "218     6958     1787           2                   Help I'm Alive   \n",
              "219     6958     1818           1                         Teenager   \n",
              "221     6958     2107           1                     Stadium Love   \n",
              "225     6958     2289           1                   Satellite Mind   \n",
              "\n",
              "                                       release      artist_name  year  \n",
              "200                               Distillation     Erin McKeown  2000  \n",
              "202                                    Sawdust      The Killers  2004  \n",
              "203                            Vampire Weekend  Vampire Weekend  2007  \n",
              "204                                Tiny Vipers      Tiny Vipers  2007  \n",
              "205                       You Know I'm No Good    Amy Winehouse  2007  \n",
              "206                                 Hell Train          Soltero     0  \n",
              "209  Old World Underground_ Where Are You Now?           Metric  2003  \n",
              "213                        Monday Morning Cold     Erin McKeown  2000  \n",
              "215                                    Give Up   Postal Service  2003  \n",
              "216                             Gimme Sympathy           Metric  2009  \n",
              "217                               Distillation     Erin McKeown  2000  \n",
              "218                                  Fantasies           Metric  2009  \n",
              "219                                    Modapop   Camera Obscura     0  \n",
              "221                                  Fantasies           Metric  2009  \n",
              "225                                  Fantasies           Metric  2009  "
            ],
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              "      <td>Fast As I Can</td>\n",
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              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_final"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uZcr1Eke2T9W"
      },
      "source": [
        "## **Exploratory Data Analysis**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ByuHmvWDeBJI"
      },
      "source": [
        "### **Let's check the total number of unique users, songs, artists in the data**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DE_gukSJ2T9W"
      },
      "source": [
        "Total number of unique user id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n5E24_Ec2T9W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4379f274-75c3-4870-eaf8-d26644e2f085"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3155"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "# Display total number of unique user_id\n",
        "df_final.user_id.nunique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wV3BOTdJII-t"
      },
      "source": [
        "Total number of unique song id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5SlpPkIE2T9W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d1c607c-8521-4102-a825-a7d7c3ea8d7a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "563"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "# Display total number of unique song_id\n",
        "df_final.song_id.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display total number of unique song titles\n",
        "df_final.title.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xmoKgoglyFnc",
        "outputId": "cb277b3d-c7ac-487e-9df2-296e12d6e818"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "561"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eGXPsCjXVpUW"
      },
      "source": [
        "Total number of unique artists"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qSVUwb8h2T9X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1752fa1b-210f-4663-9fc1-c4c6f1c80df5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "232"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "# Display total number of unique artists\n",
        "df_final.artist_name.nunique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bvk-YAo-eGGW"
      },
      "source": [
        "#### **Observations and Insights:**\n",
        "* The final shape of our dataframe is 117,876\n",
        "* **User-to-song ration:** there are significant amount of users (3,155) than unique songs (563). It is likely that these songs have been listened to by multiple users.\n",
        "* **User-activity:** with 117,876 interactions across 3155 users, each user have around 37 unique interactions with the songs.\n",
        "* **Artist-to-song ratio:** On average, 1 artist have a about 2-3 songs in the final dataset. This indicates that many artist is a 1-many relationship with songs."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rLdIfv22ISBK"
      },
      "source": [
        "### **Let's find out about the most interacted songs and interacted users**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W3DyN_8atsCx"
      },
      "source": [
        "Most interacted songs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qWDrvIFF2T9X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "outputId": "b2951316-b84d-4a9e-d59f-04df0162e583"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "song_id\n",
              "8582    751\n",
              "352     748\n",
              "2220    713\n",
              "1118    662\n",
              "4152    652\n",
              "5531    618\n",
              "4448    609\n",
              "6189    606\n",
              "6293    583\n",
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              "Name: play_count, dtype: int64"
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          "metadata": {},
          "execution_count": 31
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      ],
      "source": [
        "# Find the 10 most interacted songs\n",
        "df_final.groupby('song_id')['play_count'].count().sort_values(ascending=False).head(10)"
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "Most interacted users"
      ]
    },
    {
      "cell_type": "code",
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        "outputId": "16a3aecb-5451-461f-ea6e-c06550dcbd1f"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "user_id\n",
              "61472    243\n",
              "15733    227\n",
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              "Name: play_count, dtype: int64"
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          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "# Find the top 10 most interacted users\n",
        "df_final.groupby('user_id')['play_count'].count().sort_values(ascending=False).head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tPZRc1e-eyyO"
      },
      "source": [
        "#### **Observations and Insights:**\n",
        "We now have a much smaller and final dataset to work with.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "joFF5zndX1Dk"
      },
      "source": [
        "Songs released on yearly basis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "outputId": "e8283f1e-59a5-4eac-ddbd-05405795982e"
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              "      title\n",
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              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f2b29c89-a6da-4b30-b3d6-11754cd8cedc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "count",
              "summary": "{\n  \"name\": \"count\",\n  \"rows\": 89,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 1922,\n        \"max\": 2011,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          1967,\n          1976,\n          1953\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9778,\n        \"min\": 1,\n        \"max\": 39414,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          424,\n          6,\n          77\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "# Find out the number of songs released in a year, use the songs_df\n",
        "  # Hint: Use groupby function on the 'year' column\n",
        "count_songs = song_df.groupby('year').count()['title']\n",
        "count = pd.DataFrame(count_songs)\n",
        "count.drop(count.index[0],inplace = True)\n",
        "count.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bZCkOiAB2T9Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "outputId": "5284ae4b-86c2-44fb-de2b-9bc971303dd7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x1000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create a barplot plot with y label as \"number of titles played\" and x -axis year\n",
        "\n",
        "# Set the figure size\n",
        "plt.figure(figsize=(30, 10))\n",
        "\n",
        "\n",
        "# Set the x label of the plot\n",
        "ax = sns.barplot(data=count, x=count.index, y='title' , estimator=np.median, hue='year', color='green')\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
        "\n",
        "# Set the y label of the plot\n",
        "plt.ylabel('Number of titles played')\n",
        "\n",
        "# Set the title of the plot\n",
        "plt.title('Songs released on yearly basis')\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VUcXc7ZYfaGl"
      },
      "source": [
        "#### **Observations and Insights:** #\n",
        "* **Lower number of plays in Early years(1924-1950):** The number of songs played annually remained relatively low with a slow growth upward trend. This could be due to the limited access to music playback technologies.\n",
        "* **Sharp spike in plays 2000s:** The number of songs played grew significantly from mid 1990s and accelerated dramatically from early 2000s onwards, peaking between 2005 and 2007. This can be an attribute of digital media platform explosion like iTunes, MP3 players, and online streaming services.\n",
        "* There is a significant drop in 2011, this could be influenced by factors like music streaming services which changes the way people consume music or that the data might not be complete for 2011.  \n",
        "\n",
        "* The impact of technology right around the early 2000s is likely the reflection of internet and digital distribution. The drop might suggest a shift toward music streaming platforms that do not record \"plays\" in the same way as physical or digital downloads.\n",
        "\n",
        "* The cultural shift during the late 20th century might relfect the users' music consumption behavior - from purchasing or owning an album or track to accessing large libraries via streaming - which may affect the total number of 'titles played'.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RtAjyDMioHCp"
      },
      "source": [
        "**Think About It:** What other insights can be drawn using exploratory data analysis?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OWO4C8KsK_5e"
      },
      "source": [
        "Now that we have explored the data, let's apply different algorithms to build recommendation systems.\n",
        "\n",
        "**Note:** Use the shorter version of the data, i.e., the data after the cutoffs as used in Milestone 1."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9VThYg7voGIz"
      },
      "source": [
        "## Building various models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ituk9wA4Idib"
      },
      "source": [
        "### **Popularity-Based Recommendation Systems**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "462hsbxaI1ED"
      },
      "source": [
        "Let's take the count and sum of play counts of the songs and build the popularity recommendation systems based on the sum of play counts."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UXhBZlDE-jEu"
      },
      "outputs": [],
      "source": [
        "# Calculating average play_count\n",
        "       # Hint: Use groupby function on the song_id column\n",
        "average_count = df_final.groupby('song_id')['play_count'].mean()\n",
        "# Calculating the frequency a song is played\n",
        "      # Hint: Use groupby function on the song_id column\n",
        "play_freq = df_final.groupby('song_id')['play_count'].count()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Average play count by song id:\\n {average_count.head()}\\n')\n",
        "print(f'Play Frequency by song id:\\n {play_freq.head()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1LcuhAOQqMSs",
        "outputId": "cac8aaeb-f965-4eac-b53a-014003b18cc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average play count by song id:\n",
            " song_id\n",
            "21    1.622642\n",
            "22    1.492424\n",
            "52    1.729216\n",
            "62    1.728070\n",
            "93    1.452174\n",
            "Name: play_count, dtype: float64\n",
            "\n",
            "Play Frequency by song id:\n",
            " song_id\n",
            "21    265\n",
            "22    132\n",
            "52    421\n",
            "62    114\n",
            "93    115\n",
            "Name: play_count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v2XYdXvWdyys",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "751a01cd-01ba-48db-dcdc-e7c298222c09"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         avg_count  play_freq\n",
              "song_id                      \n",
              "21        1.622642        265\n",
              "22        1.492424        132\n",
              "52        1.729216        421\n",
              "62        1.728070        114\n",
              "93        1.452174        115"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cbc35aa3-dacb-4646-82d2-2dbfd39071f1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>avg_count</th>\n",
              "      <th>play_freq</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>song_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1.622642</td>\n",
              "      <td>265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1.492424</td>\n",
              "      <td>132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>1.729216</td>\n",
              "      <td>421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>1.728070</td>\n",
              "      <td>114</td>\n",
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              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>1.452174</td>\n",
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              "      const quickchartButtonEl =\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "final_play",
              "summary": "{\n  \"name\": \"final_play\",\n  \"rows\": 563,\n  \"fields\": [\n    {\n      \"column\": \"song_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2900,\n        \"min\": 21,\n        \"max\": 9989,\n        \"num_unique_values\": 563,\n        \"samples\": [\n          4462,\n          9076,\n          4806\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.21765381446219467,\n        \"min\": 1.238709677419355,\n        \"max\": 3.3738317757009346,\n        \"num_unique_values\": 535,\n        \"samples\": [\n          1.7465753424657535,\n          1.4830508474576272,\n          1.6343115124153498\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"play_freq\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 117,\n        \"min\": 96,\n        \"max\": 751,\n        \"num_unique_values\": 238,\n        \"samples\": [\n          116,\n          176,\n          218\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "from typing_extensions import final\n",
        "# Making a dataframe with the average_count and play_freq\n",
        "final_play = pd.DataFrame({'avg_count': average_count, 'play_freq': play_freq})\n",
        "# Let us see the first five records of the final_play dataset\n",
        "final_play.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WnCT-A7RK_5g"
      },
      "source": [
        "Now, let's create a function to find the top n songs for a recommendation based on the average play count of song. We can also add a threshold for a minimum number of playcounts for a song to be considered for recommendation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QiT9FV3GNCrb"
      },
      "outputs": [],
      "source": [
        "# Build the function to find top n songs\n",
        "def top_n_songs(df, n, min_playcounts):\n",
        "    # Find top songs by comparing the play frequency and sort by average count\n",
        "    top_songs = df[df['play_freq'] >= min_playcounts].sort_values(by='avg_count', ascending=False).head(n)\n",
        "    return top_songs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GpZt_BeXgz4F",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "3a60310e-9ed7-412a-c371-225e577e14df"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         avg_count  play_freq\n",
              "song_id                      \n",
              "7224      3.373832        107\n",
              "6450      2.578431        102\n",
              "9942      2.486667        150\n",
              "5531      2.309061        618\n",
              "5653      2.296296        108\n",
              "8483      2.235772        123\n",
              "2220      2.220196        713\n",
              "657       2.218543        151\n",
              "614       2.217158        373\n",
              "352       2.184492        748"
            ],
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('top_10_songs')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('top_10_songs');\n",
              "      }\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "top_10_songs",
              "summary": "{\n  \"name\": \"top_10_songs\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"song_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3510,\n        \"min\": 352,\n        \"max\": 9942,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          614,\n          6450,\n          8483\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3616892759515749,\n        \"min\": 2.1844919786096257,\n        \"max\": 3.3738317757009346,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          2.2171581769436997,\n          2.5784313725490198,\n          2.2357723577235773\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"play_freq\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271,\n        \"min\": 102,\n        \"max\": 748,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          373,\n          102,\n          123\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "# Recommend top 10 songs using the function defined above\n",
        "top_10_songs = top_n_songs(final_play, 10, 100)\n",
        "top_10_songs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gf13HrPPJeWT"
      },
      "source": [
        "### **User User Similarity-Based Collaborative Filtering**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ROcEpduohdua"
      },
      "source": [
        "To build the user-user-similarity-based and subsequent models we will use the \"surprise\" library."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aKLrKn8IfGjk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00123c49-7c69-4338-b6df-a3feaeb9db44"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting surprise\n",
            "  Downloading surprise-0.1-py2.py3-none-any.whl.metadata (327 bytes)\n",
            "Collecting scikit-surprise (from surprise)\n",
            "  Downloading scikit_surprise-1.1.4.tar.gz (154 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/154.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.4/154.4 kB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.4.2)\n",
            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.13.1)\n",
            "Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\n",
            "Building wheels for collected packages: scikit-surprise\n",
            "  Building wheel for scikit-surprise (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for scikit-surprise: filename=scikit_surprise-1.1.4-cp310-cp310-linux_x86_64.whl size=2357284 sha256=9b2317b17f559fb5dc58ebd3e1eb835a0952a096638d0b337e39dbe99232f453\n",
            "  Stored in directory: /root/.cache/pip/wheels/4b/3f/df/6acbf0a40397d9bf3ff97f582cc22fb9ce66adde75bc71fd54\n",
            "Successfully built scikit-surprise\n",
            "Installing collected packages: scikit-surprise, surprise\n",
            "Successfully installed scikit-surprise-1.1.4 surprise-0.1\n"
          ]
        }
      ],
      "source": [
        "# Install the surprise package using pip. Uncomment and run the below code to do the same\n",
        "\n",
        "!pip install surprise"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UJ1wEylUpexj"
      },
      "outputs": [],
      "source": [
        "# Import necessary libraries\n",
        "\n",
        "# To compute the accuracy of models\n",
        "from surprise import accuracy\n",
        "\n",
        "# For creating the train and test dataset\n",
        "from surprise.model_selection import train_test_split\n",
        "\n",
        "# This class is used to parse a file containing play_counts, data should be in structure - user; item; play_count\n",
        "from surprise.reader import Reader\n",
        "\n",
        "\n",
        "# Class for loading datasets\n",
        "from surprise.dataset import Dataset\n",
        "\n",
        "# For tuning model hyperparameters\n",
        "from surprise.model_selection import GridSearchCV\n",
        "\n",
        "\n",
        "# For implementing similarity-based recommendation system\n",
        "from surprise.prediction_algorithms import KNNBasic\n",
        "\n",
        "# For implementing matrix factorization based recommendation system\n",
        "from surprise.prediction_algorithms.matrix_factorization import SVD\n",
        "\n",
        "# For implementing KFold cross-validation\n",
        "from surprise.model_selection import KFold\n",
        "\n",
        "# For implementing clustering-based recommendation system\n",
        "from surprise import CoClustering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jBW4BUhWTsnm"
      },
      "source": [
        "### Some useful functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZhFa_4aHHchr"
      },
      "source": [
        "Below is the function to calculate precision@k and recall@k, RMSE, and F1_Score@k to evaluate the model performance."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZOvOgjGWrMVV"
      },
      "source": [
        "**Think About It:** Which metric should be used for this problem to compare different models?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rxn-GahOTsnm"
      },
      "outputs": [],
      "source": [
        "def precision_recall_at_k(model, k=30, threshold=1.5):\n",
        "    \"\"\"Return precision and recall at k metrics for each user\"\"\"\n",
        "\n",
        "    # First map the predictions to each user.\n",
        "    user_est_true = defaultdict(list)\n",
        "\n",
        "    #Making predictions on the test data\n",
        "    predictions = model.test(testset)\n",
        "\n",
        "    for uid, _, true_r, est, _ in predictions:\n",
        "        user_est_true[uid].append((est, true_r))\n",
        "\n",
        "    precisions = dict()\n",
        "    recalls = dict()\n",
        "    for uid, playing_count in user_est_true.items():\n",
        "\n",
        "        # Sort play count by estimated value\n",
        "        playing_count.sort(key=lambda x: x[0], reverse=True)\n",
        "\n",
        "        # Number of relevant items\n",
        "        n_rel = sum((true_r >= threshold) for (_, true_r) in playing_count)\n",
        "\n",
        "        # Number of recommended items in top k\n",
        "        n_rec_k = sum((est >= threshold) for (est, _) in playing_count[:k])\n",
        "\n",
        "        # Number of relevant and recommended items in top k\n",
        "        n_rel_and_rec_k = sum(((true_r >= threshold) and (est >= threshold))\n",
        "                              for (est, true_r) in playing_count[:k])\n",
        "\n",
        "        # Precision@K: Proportion of recommended items that are relevant\n",
        "        # When n_rec_k is 0, Precision is undefined. We here set Precision to 0 when n_rec_k is 0.\n",
        "\n",
        "        precisions[uid] = n_rel_and_rec_k / n_rec_k if n_rec_k != 0 else 0\n",
        "\n",
        "        # Recall@K: Proportion of relevant items that are recommended\n",
        "        # When n_rel is 0, Recall is undefined. We here set Recall to 0 when n_rel is 0.\n",
        "\n",
        "        recalls[uid] = n_rel_and_rec_k / n_rel if n_rel != 0 else 0\n",
        "\n",
        "    #Mean of all the predicted precisions are calculated.\n",
        "    precision = round((sum(prec for prec in precisions.values()) / len(precisions)),3)\n",
        "    #Mean of all the predicted recalls are calculated.\n",
        "    recall = round((sum(rec for rec in recalls.values()) / len(recalls)),3)\n",
        "\n",
        "    accuracy.rmse(predictions)\n",
        "    print('Precision: ', precision) #Command to print the overall precision\n",
        "    print('Recall: ', recall) #Command to print the overall recall\n",
        "    print('F_1 score: ', round((2*precision*recall)/(precision+recall),3)) # Formula to compute the F-1 score."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PcmLRxH4IjfG"
      },
      "source": [
        "**Think About It:** In the function precision_recall_at_k above the threshold value used is 1.5. How precision and recall are affected by changing the threshold? What is the intuition behind using the threshold value of 1.5?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HHU0CXx2huhh"
      },
      "source": [
        "Below we are loading the **dataset**, which is a **pandas dataframe**, into a **different format called `surprise.dataset.DatasetAutoFolds`** which is required by this library. To do this we will be **using the classes `Reader` and `Dataset`**\n",
        "\n",
        "You will also notice here that we read the dataset by providing a scale of ratings. However, as you would know, we do not have ratings data of the songs. In this case, we are going to use play_count as a proxy for ratings with the assumption that the more the user listens to a song, the higher the chance that they like the song"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rGfYDiOCpe4X"
      },
      "outputs": [],
      "source": [
        "# Instantiating Reader scale with expected rating scale\n",
        " #use rating scale (0, 5)\n",
        "reader = Reader(rating_scale=(0, 5))\n",
        "# Loading the dataset\n",
        " # Take only \"user_id\",\"song_id\", and \"play_count\"\n",
        "data = Dataset.load_from_df(df_final[['user_id', 'song_id', 'play_count']], reader)\n",
        "# Splitting the data into train and test dataset\n",
        " # Take test_size = 0.4, random_state = 42\n",
        "trainset, testset = train_test_split(data, test_size=0.4, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CuTmLjUP1aED"
      },
      "source": [
        "**Think About It:** How changing the test size would change the results and outputs?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vO3FL7iape8A",
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "97a44784-0c10-4952-8d3d-c0886b731efa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Computing the cosine similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "RMSE: 1.0878\n",
            "Precision:  0.396\n",
            "Recall:  0.692\n",
            "F_1 score:  0.504\n"
          ]
        }
      ],
      "source": [
        "# Build the default user-user-similarity model\n",
        "sim_options = {'name': 'cosine',\n",
        "               'user_based': True}\n",
        "\n",
        "# KNN algorithm is used to find desired similar items\n",
        " # Use random_state = 1\n",
        "algo_knn_user_user = KNNBasic(sim_options=sim_options, random_state=1)\n",
        "\n",
        "# Train the algorithm on the trainset, and predict play_count for the testset\n",
        "algo_knn_user_user.fit(trainset)\n",
        "predictions = algo_knn_user_user.test(testset)\n",
        "\n",
        "# Let us compute precision@k, recall@k, and f_1 score with k = 30\n",
        " # Use sim_user_user model\n",
        "precision_recall_at_k(algo_knn_user_user, k=30, threshold=1.5)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Look at data from the predicted play_count for the test dataset\n",
        "test_prediction = pd.DataFrame(predictions)\n",
        "test_prediction.head(15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "iU9Bsgao16vz",
        "outputId": "38f8c906-1333-4f5d-a44e-c2228fe582f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      uid   iid  r_ui       est                                    details\n",
              "0   26071  7103   1.0  1.550000  {'actual_k': 40, 'was_impossible': False}\n",
              "1   51242  8224   1.0  1.227385  {'actual_k': 40, 'was_impossible': False}\n",
              "2   72299   473   1.0  1.642196  {'actual_k': 38, 'was_impossible': False}\n",
              "3   28196  1354   1.0  1.400000  {'actual_k': 40, 'was_impossible': False}\n",
              "4   63083  4949   1.0  1.699356  {'actual_k': 40, 'was_impossible': False}\n",
              "5   70828  7628   1.0  1.957346  {'actual_k': 40, 'was_impossible': False}\n",
              "6   72912   892   1.0  1.224254  {'actual_k': 40, 'was_impossible': False}\n",
              "7   18177  9386   1.0  1.377078  {'actual_k': 40, 'was_impossible': False}\n",
              "8   74185  9096   1.0  1.774082  {'actual_k': 40, 'was_impossible': False}\n",
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            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mzcdlWmer6GA"
      },
      "source": [
        "**Observations and Insights:**\n",
        "* **RMSE of 1.0878** suggest moderate accuracy.\n",
        "* Quite **low precision** (0.396). This implies that the model recommends many false positives, meaning many irrelevant songs.\n",
        "* The **recall seems quite high (0.69)** which, in contrast with the low precision, shows that it captures a large portion of items that the user might be interested.   \n",
        "* The **F1 score of 0.504** inidcates that the model has a fair balance and leans toward over-recommending songs to improve recall, which may compromise the recommendations in terms of precision.\n",
        "* We can try to **fine tuned** this by improving the presicion using hybrid models that combines user-user similarity with **item-based collaborative filtering** or **content-based filtering**.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sxd23bZ9pe_x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26944810-bc51-456c-d050-01e1ea535f6d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user: 54309      item: 6293       r_ui = 3.00   est = 1.55   {'actual_k': 40, 'was_impossible': False}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Prediction(uid=54309, iid=6293, r_ui=3, est=1.55, details={'actual_k': 40, 'was_impossible': False})"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "# Predicting play_count for a sample user with a listened song\n",
        "# Use any user id  and song_id - let's try id 6958 and song_id 1671\n",
        "algo_knn_user_user.predict(uid=54309, iid=6293, r_ui = 3, verbose=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PbFcBj1PpfEV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "264fd214-5f01-42f5-8f2a-462991b20f54"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user: 54309      item: 3232       r_ui = None   est = 1.55   {'actual_k': 40, 'was_impossible': False}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Prediction(uid=54309, iid=3232, r_ui=None, est=1.5493898116819662, details={'actual_k': 40, 'was_impossible': False})"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "# Predicting play_count for a sample user with a song not-listened by the user\n",
        " #predict play_count for any sample user\n",
        "algo_knn_user_user.predict(uid=54309, iid=3232, verbose=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P9EVM7DysC47"
      },
      "source": [
        "**Observations and Insights:**\n",
        "* Prediction with true data seemed **underestimated**. There is large error in the first prediction for song_id 6293 at 1.55 where the true play count is 3. This suggests that the model may **not be effectively capturing** the user's true preference.\n",
        "* There are **40 similar neighbors** for both predicitons, but the model may not be identifying the most relevant users for accurate predictions, causing this significant underestimation.\n",
        "* This could be **contributing to the low precision score (0.396)** from the overall evaluation, as it might be recommending songs that aren't actually relevant to the user."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lt1QBiylsIOm"
      },
      "source": [
        "Now, let's try to tune the model and see if we can improve the model performance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T3diJPL7-tVw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a62764f0-cadc-4aea-9ef6-7f52b0ddaf27"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best RMSE Score: 1.023\n",
            "Combination of parameters that gave the best RMSE score: {'k': 30, 'min_k': 6, 'sim_options': {'name': 'pearson_baseline', 'user_based': False, 'min_support': 2}}\n"
          ]
        }
      ],
      "source": [
        "# Setting up parameter grid to tune the hyperparameters\n",
        "param_grid = {'k': [10, 20, 30], 'min_k': [3,6,9],\n",
        "              'sim_options': {'name': ['cosine', 'pearson', 'pearson_baseline'],\n",
        "                              'user_based': [True, False], 'min_support' : [2, 4]}\n",
        "              }\n",
        "\n",
        "# Performing 3-fold cross-validation to tune the hyperparameters\n",
        "user_user_grid = GridSearchCV(KNNBasic, param_grid, measures=['rmse'], cv=3, n_jobs=-1)\n",
        "\n",
        "# Fitting the data\n",
        " # Use entire data for GridSearch\n",
        "user_user_grid.fit(data)\n",
        "\n",
        "# Best RMSE score\n",
        "print(f'Best RMSE Score: {round(user_user_grid.best_score[\"rmse\"],3)}')\n",
        "\n",
        "# Combination of parameters that gave the best RMSE score\n",
        "print(f'Combination of parameters that gave the best RMSE score: {user_user_grid.best_params[\"rmse\"]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PujRJA8X_JEJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1217e995-6fcb-4227-82d7-7a979a045969"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Estimating biases using als...\n",
            "Computing the pearson_baseline similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "RMSE: 1.0328\n",
            "Precision:  0.408\n",
            "Recall:  0.665\n",
            "F_1 score:  0.506\n"
          ]
        }
      ],
      "source": [
        "# Train the best model found in above gridsearch\n",
        "user_user_optimized = KNNBasic(sim_options={'name':'pearson_baseline', 'user_based':False, 'min_support':2},k=30,min_k=6)\n",
        "\n",
        "# Training the algorithm on the trainset\n",
        "user_user_optimized.fit(trainset)\n",
        "\n",
        "# Compute precision@k\n",
        "precision_recall_at_k(user_user_optimized)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MH5OBZ7Nse6m"
      },
      "source": [
        "**Observations and Insights:**\n",
        "* The **RSME is slightly lower (1.0328 from 1.087)**, which indicates that the model provides a more accurate prediciton overall.\n",
        "* Although the precision is better, the recall is slightly lower than the baseline user-user similarity model. This indicates that the model is more **selective with its recommendation**.\n",
        "* **Better balance for F1 score 0.506**, this could be the reflection of better improvement in the quality of the recommendation.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FgV63lHiq1TV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81a5ab20-0c59-45ab-d3a8-74f1e9cb7fac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user: 6958       item: 1671       r_ui = 2.00   est = 1.96   {'actual_k': 10, 'was_impossible': False}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Prediction(uid=6958, iid=1671, r_ui=2, est=1.9634957386781853, details={'actual_k': 10, 'was_impossible': False})"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "# Predict the play count for a user who has listened to the song. Take user_id 6958, song_id 1671 and r_ui = 2\n",
        "user_user_optimized.predict(uid=6958, iid=1671, r_ui = 2, verbose=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HXO2Ztjhq1bN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77bc9c60-bb6a-402f-9b37-f682fddb943d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user: 6958       item: 3232       r_ui = None   est = 1.28   {'actual_k': 10, 'was_impossible': False}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Prediction(uid=6958, iid=3232, r_ui=None, est=1.2759946618244609, details={'actual_k': 10, 'was_impossible': False})"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "# Predict the play count for a song that is not listened to by the user (with user_id 6958)\n",
        "user_user_optimized.predict(uid=6958, iid=3232, verbose=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SdpJ--8QWuzz"
      },
      "source": [
        "**Observations and Insights:**\n",
        "* The **accuracy for known rating has drastically improved** for song_id 1671. The prediction of 1.96 is very close to the actual play count of 2.\n",
        "* For song 3232, the model is **conservative in its prediction (1.28)**. This could indicate taht he similar users used for this prediction either didn't play this song as much or the user's preference are less aligned with the user of this particular song.\n",
        "* **Use of 10 similar users** indicates that the system is basing its predictions on a reasonable number of similar users."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SQ9M4pplNbWS"
      },
      "source": [
        "**Think About It:** Along with making predictions on listened and unknown songs can we get 5 nearest neighbors (most similar) to a certain song?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TbFle7cKmBJG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0052bed1-72c5-4590-fbef-1ede3f46ac1e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[124, 523, 173, 205, 65]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "# Use inner id 0\n",
        "user_user_optimized.get_neighbors(0, k=5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U3ESobDynVNI"
      },
      "source": [
        "Below we will be implementing a function where the input parameters are:\n",
        "\n",
        "- data: A **song** dataset\n",
        "- user_id: A user-id **against which we want the recommendations**\n",
        "- top_n: The **number of songs we want to recommend**\n",
        "- algo: The algorithm we want to use **for predicting the play_count**\n",
        "- The output of the function is a **set of top_n items** recommended for the given user_id based on the given algorithm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vW9V1Tk65HlY"
      },
      "outputs": [],
      "source": [
        "def get_recommendations(data, user_id, top_n, algo):\n",
        "\n",
        "    # Creating an empty list to store the recommended song ids\n",
        "    recommendations =[]\n",
        "\n",
        "    # Creating an user item interactions matrix\n",
        "    user_item_interactions_matrix = data.pivot(index='user_id', columns='song_id', values='play_count')\n",
        "\n",
        "    # Extracting those song ids which the user_id has not played yet\n",
        "    non_interacted_songs = user_item_interactions_matrix.loc[user_id][user_item_interactions_matrix.loc[user_id].isnull()].index.tolist()\n",
        "\n",
        "    # Looping through each of the song ids which user_id has not interacted yet\n",
        "    for item_id in non_interacted_songs:\n",
        "\n",
        "        # Predicting the users for those non played song ids by this user\n",
        "        est = algo.predict(user_id, item_id).est\n",
        "\n",
        "        # Appending the predicted play_counts\n",
        "        recommendations.append((item_id, est))\n",
        "    # Sorting the predicted play_counts in descending order\n",
        "    recommendations.sort(key=lambda x: x[1], reverse=True)\n",
        "\n",
        "    return recommendations[:top_n] # Returing top n highest predicted play_count songs for this user\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qWbR85mI5Hrk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "05566c45-684a-4f9c-9dce-2efee0623866"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(2342, 2.653903109477311),\n",
              " (5101, 2.386577162060947),\n",
              " (139, 2.3137269182649587),\n",
              " (7519, 2.2708636509919065),\n",
              " (8099, 2.2127017409877365)]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "# Make top 5 recommendations for any user_id with a similarity-based recommendation engine\n",
        "recommendations = get_recommendations(df_final, 6958, 5, user_user_optimized)\n",
        "recommendations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b5WfIX0Z6_q2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "f3ce7ff6-ca7c-4010-f3fc-e479c42a1cd6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   song_id  predicted_play_count\n",
              "0     2342              2.653903\n",
              "1     5101              2.386577\n",
              "2      139              2.313727\n",
              "3     7519              2.270864\n",
              "4     8099              2.212702"
            ],
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              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"song_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3391,\n        \"min\": 139,\n        \"max\": 8099,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5101,\n          8099,\n          139\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_play_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1721796254152417,\n        \"min\": 2.2127017409877365,\n        \"max\": 2.653903109477311,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.386577162060947,\n          2.2127017409877365,\n          2.3137269182649587\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "# Building the dataframe for above recommendations with columns \"song_id\" and \"predicted_play_count\"\n",
        "pd.DataFrame(recommendations, columns=['song_id', 'predicted_play_count'])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kyhThMOttWjj"
      },
      "source": [
        "**Observations and Insights:**\n",
        "* Overall, the model provides a set of recommendations with good relevance to the user's preference, although the narrow range of predicted play counts suggests there is room to enhance the ranking clarity."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ghwEJY2e7INB"
      },
      "source": [
        "### Correcting the play_counts and Ranking the above songs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "39Hs7ZbO9v3O"
      },
      "outputs": [],
      "source": [
        "def ranking_songs(recommendations, playing_count):\n",
        "  # Sort the songs based on play counts\n",
        "  ranked_songs = playing_count.loc[[items[0] for items in recommendations]].sort_values('play_freq', ascending = False)[['play_freq']].reset_index()\n",
        "  # The recommendations data is a tuple and we're assuming the first element is the song_id (without column name)\n",
        "\n",
        "  # Merge with the recommended songs to get predicted play_counts - use inner join to return only existing values on both dataframes\n",
        "  ranked_songs = ranked_songs.merge(pd.DataFrame(recommendations, columns=['song_id', 'predicted_play_count']), on='song_id', how='inner')\n",
        "\n",
        "\n",
        "  # Rank the songs based on corrected play_counts\n",
        "  ranked_songs['corrected_play_count'] = ranked_songs['predicted_play_count'] - 1 / np.sqrt(ranked_songs['play_freq'])\n",
        "\n",
        "  # Sort the songs based on corrected play_counts\n",
        "  ranked_songs = ranked_songs.sort_values(by='corrected_play_count', ascending=False)\n",
        "\n",
        "  # Return the top n songs\n",
        "  return ranked_songs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zQvst41lOoMX"
      },
      "source": [
        "**Think About It:** In the above function to correct the predicted play_count a quantity 1/np.sqrt(n) is subtracted. What is the intuition behind it? Is it also possible to add this quantity instead of subtracting?"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This formula penalizes songs that have a high play_freq by subtracting a value that is inversely proportional to the square root of play_freq. The more frequently a song has been played, the larger the correction factor, resulting in a slightly lower corrected play count.\n",
        "\n",
        "Effect: This adjustment helps avoid over-ranking songs that have been played a lot simply because they are very popular, allowing other songs with lower play counts but high predicted counts to rank higher."
      ],
      "metadata": {
        "id": "dim0_mexxtok"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xoiAL_vH8miC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "583b11dd-b7ee-428c-c32c-50830f85c6f2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   song_id  play_freq  predicted_play_count  corrected_play_count\n",
              "4     2342        111              2.653903              2.558987\n",
              "2     5101        130              2.386577              2.298871\n",
              "3      139        119              2.313727              2.222057\n",
              "1     7519        168              2.270864              2.193712\n",
              "0     8099        275              2.212702              2.152399"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"ranking_songs(recommendations, final_play)\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"song_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3391,\n        \"min\": 139,\n        \"max\": 8099,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5101,\n          8099,\n          139\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"play_freq\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 67,\n        \"min\": 111,\n        \"max\": 275,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          130,\n          275,\n          119\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_play_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1721796254152417,\n        \"min\": 2.2127017409877365,\n        \"max\": 2.653903109477311,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.386577162060947,\n          2.2127017409877365,\n          2.3137269182649587\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"corrected_play_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1621260817583845,\n        \"min\": 2.152399472072184,\n        \"max\": 2.558987309902061,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.298871360130244,\n          2.152399472072184,\n          2.2220570685621377\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "# Applying the ranking_songs function on the final_play data\n",
        "ranking_songs(recommendations, final_play)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KOwwGsH8toLG"
      },
      "source": [
        "**Observations and Insights:**\n",
        "* **High play frequency resulted in lower correction**. Song 8099 has the highest play frequency, after the correction formula the corrected play_count is reduced 2.15. This tells us that this song is popular and overplayed and we don't want this to dominate the ranking because of high frequency.\n",
        "* **Lower play_freq means smaller adjustments**. For instance song 2342 is still at the top and very small correction is applied.\n",
        "* Final ranking is based on the corrected play count which makes out **ranking less biased**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QgbzJKk7Tsnr"
      },
      "source": [
        "### Item Item Similarity-based collaborative filtering recommendation systems"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W5RMcdzjTsns",
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "054039a7-bcfb-41d9-fa23-ca3575f45584"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 1.0394\n",
            "Precision:  0.307\n",
            "Recall:  0.562\n",
            "F_1 score:  0.397\n"
          ]
        }
      ],
      "source": [
        "# Apply the item-item similarity collaborative filtering model with random_state = 1 and evaluate the model performance\n",
        "\n",
        "# Declaring similarity options\n",
        "sim_options = {'name': 'cosine',\n",
        "               'user_based': False}\n",
        "\n",
        "# KNN algorithm is used to find desired similar items. Use random_state=1\n",
        "algo_knn_item = KNNBasic(sim_options=sim_options, verbose=False, random_state=1)\n",
        "\n",
        "# Train the algorithm on the trainset, and predict ratings for the test set\n",
        "algo_knn_item.fit(trainset)\n",
        "\n",
        "# Let us compute precision@k, recall@k, and f_1 score with k = 10\n",
        "precision_recall_at_k(algo_knn_item)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JfdIJ6XWunx0"
      },
      "source": [
        "**Observations and Insights:**\n",
        "* ***RSME at 1.04** means that on average, the predicted play counts differ from actual play counts by 1.04, suggesting moderate accuracy.\n",
        "* **Precision is still relatively low at 0.30**, it inidicates that the model is not very good at recommending relevant songs.\n",
        "* **Recall of 0.56** indicates that the system captures 56.2% of relevant songs in the dataset. This is fairly good at covering broad range of relevant songs.\n",
        "* **F1 score of 0.397** is pulled down by low precision, suggesting that the system recommends a lot of irrelevant song along with relevant ones.\n",
        "* To **improve the recommendation system** we need to focus on improving the precision, perhaps we need to adjust the similarity thresholds or tuning our algorithm to find the best similarity metric or neighborhood size.  \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5yILOxXRTsns",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da893f09-60e8-45aa-9066-7b0d9ee95518"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user: 6958       item: 1671       r_ui = 2.00   est = 1.36   {'actual_k': 20, 'was_impossible': False}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Prediction(uid=6958, iid=1671, r_ui=2, est=1.3614157231762556, details={'actual_k': 20, 'was_impossible': False})"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "# Predicting play count for a sample user_id 6958 and song (with song_id 1671) listened to by the user\n",
        "algo_knn_item.predict(uid=6958, iid=1671, r_ui = 2, verbose=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jSn8oK3JZsTc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "187e3e10-b0ed-41ab-d085-6e255fb7c2d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user: 6958       item: 1671       r_ui = None   est = 1.36   {'actual_k': 20, 'was_impossible': False}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Prediction(uid=6958, iid=1671, r_ui=None, est=1.3614157231762556, details={'actual_k': 20, 'was_impossible': False})"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "# Predict the play count for a user that has not listened to the song (with song_id 1671)\n",
        "algo_knn_item.predict(uid=6958, iid=1671, verbose=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dxE9fJ8Dupby"
      },
      "source": [
        "**Observations and Insights:**\n",
        "* The prediction of 1.38 is significantly lower than the actual play count. This suggests that the model may **not be capturing all relevant factors** that influences the user's preference.\n",
        "* **Limited accuracy for un-interacted songs**. The second prediction provides the same estimate, which indicates that the model may not provide enough diversity in the prediciton process.\n",
        "* **Improving precision and the ability to predict** user preferences for specific songs will help enhance the overall recommendation. Incorporating additional features or switching to a hybrid model might help address these limitations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f5bcZ3HgTsnt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "544d6775-2eb3-484d-b55d-02a06bd53909"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best RMSE Score: 1.023\n",
            "Best MAE Score: 0.742\n",
            "Combination of parameters that gave the best RMSE score: {'k': 30, 'min_k': 6, 'sim_options': {'name': 'pearson_baseline', 'user_based': False, 'min_support': 2}}\n"
          ]
        }
      ],
      "source": [
        "# Apply grid search for enhancing model performance\n",
        "\n",
        "# Setting up parameter grid to tune the hyperparameters\n",
        "param_grid = {'k': [10, 20, 30], 'min_k': [3,6,9],\n",
        "              'sim_options': {'name': ['cosine', 'msd', 'pearson', 'pearson_baseline'], 'user_based': [False], 'min_support':[2,4]}\n",
        "              }\n",
        "\n",
        "# Performing 3-fold cross-validation to tune the hyperparameters\n",
        "grid_obj = GridSearchCV(KNNBasic, param_grid, measures=['rmse', 'mae'], cv=3, n_jobs=-1)\n",
        "\n",
        "# Fitting the data\n",
        "grid_obj.fit(data)\n",
        "\n",
        "\n",
        "# Find the best RMSE score\n",
        "print(f'Best RMSE Score: {round(grid_obj.best_score[\"rmse\"],3)}')\n",
        "\n",
        "# Find the best MAE score\n",
        "print(f'Best MAE Score: {round(grid_obj.best_score[\"mae\"],3)}')\n",
        "\n",
        "# Extract the combination of parameters that gave the best RMSE score\n",
        "print(f'Combination of parameters that gave the best RMSE score: {grid_obj.best_params[\"rmse\"]}')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* MAE is generally less sensitive to large errors than RMSE. The MAE score of 0.74 suggests that the model is fairly reliable in predicting the songs. While most of the predictions are close to the actual values, the system can be improved by fine-tuning accuracy.\n",
        "* The model suggests min_support = 2 means that at least 2 users must have rated both items for the similarity between them to be computed."
      ],
      "metadata": {
        "id": "KsqmDZK-BUuD"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SXLxjLEQYvWk"
      },
      "source": [
        "**Think About It:** How do the parameters affect the performance of the model? Can we improve the performance of the model further? Check the list of hyperparameters [here](https://surprise.readthedocs.io/en/stable/knn_inspired.html)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dSeiM1qeTsnt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3afe92d3-a5ad-48fd-d4b1-47e4407465e6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 1.0328\n",
            "Precision:  0.408\n",
            "Recall:  0.665\n",
            "F_1 score:  0.506\n"
          ]
        }
      ],
      "source": [
        "# Apply the best model found in the grid search\n",
        "sim_options = {'name': 'pearson_baseline', 'user_based': False, 'min_support': 2}\n",
        "\n",
        "# Creating an instance of KNNBasic with optimal hyperparameter values\n",
        "algo_knn_item_optimized = KNNBasic(sim_options=sim_options, k=30, min_k=6, verbose=False)\n",
        "\n",
        "#Training the algorithm on the trainset\n",
        "algo_knn_item_optimized.fit(trainset)\n",
        "\n",
        "# Let us compute precision@k, recall@k, and f_1 score with k = 10\n",
        "precision_recall_at_k(algo_knn_item_optimized)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XxXelRIluvfh"
      },
      "source": [
        "**Observations and Insights:**\n",
        "* The **RSME score is slightly improved** from 1.0394 to 1.0328. Although the difference is not significant, but it suggests that the tuning helped the model slightly.\n",
        "* The **precision and recall is also slightly better** (Precision from 0.307 to 0.408 and Recall from 0.562 to 0.665). The improvement is relevantly small but meaningful. There is better improvements to the number of relevant songs.\n",
        "* The **F1 score is now 0.506 from 0.397** suggests that the trade-off between precision and recall is better, making the recommendation more effective overall.   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gIBRRvdoTsnt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70c2e068-877f-4de9-96cc-31091726df63"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user: 6958       item: 1671       r_ui = 2.00   est = 1.96   {'actual_k': 10, 'was_impossible': False}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Prediction(uid=6958, iid=1671, r_ui=2, est=1.9634957386781853, details={'actual_k': 10, 'was_impossible': False})"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "source": [
        "# Predict the play_count by a user(user_id 6958) for the song (song_id 1671)\n",
        "algo_knn_item_optimized.predict(uid=6958, iid=1671, r_ui = 2, verbose=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LNEgcI9PTsnu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7a2cf3c-d069-4bd3-ef98-525a66e4358a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user: 6958       item: 3232       r_ui = None   est = 1.28   {'actual_k': 10, 'was_impossible': False}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Prediction(uid=6958, iid=3232, r_ui=None, est=1.2759946618244609, details={'actual_k': 10, 'was_impossible': False})"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "# Predicting play count for a sample user_id 6958 with song_id 3232 which is not listened to by the user\n",
        "algo_knn_item_optimized.predict(uid=6958, iid=3232, verbose=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yf3kDSepuwcw"
      },
      "source": [
        "**Observations and Insights:**\n",
        "* **With real user input of 2**, the model seemed to make closer prediction to the play_count of 1.96, given that the model predicted based on 10 similar songs, this is a much better estimation.\n",
        "* In the second prediction, **the estimation of 1.28** based on 10 similar songs indicates that the user is **unlikely** going to listen to this song."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZRJS4oDFTsnu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72995f0d-687d-4dd7-dc7d-d7706ded1d37"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[124, 523, 173, 205, 65]"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "# Find five most similar items to the item with inner id 0\n",
        "algo_knn_item_optimized.get_neighbors(0, k=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rzoEbuZFTsnu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9904d8b0-7838-4209-eb55-e1d3afd5fdbb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(2342, 2.653903109477311),\n",
              " (5101, 2.386577162060947),\n",
              " (139, 2.3137269182649587),\n",
              " (7519, 2.2708636509919065),\n",
              " (8099, 2.2127017409877365)]"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "# Making top 5 recommendations for any user_id  with item_item_similarity-based recommendation engine\n",
        "song_item_recommendation = get_recommendations(df_final, 6958, 5, algo_knn_item_optimized)\n",
        "song_item_recommendation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_kXVTiysTsnv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "f9463f74-c1a2-4a30-c24b-3d346ebd9722"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   song_id  predicted_play_count\n",
              "0     2342              2.653903\n",
              "1     5101              2.386577\n",
              "2      139              2.313727\n",
              "3     7519              2.270864\n",
              "4     8099              2.212702"
            ],
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"song_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3391,\n        \"min\": 139,\n        \"max\": 8099,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5101,\n          8099,\n          139\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_play_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1721796254152417,\n        \"min\": 2.2127017409877365,\n        \"max\": 2.653903109477311,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.386577162060947,\n          2.2127017409877365,\n          2.3137269182649587\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "# Building the dataframe for above recommendations with columns \"song_id\" and \"predicted_play_count\"\n",
        "pd.DataFrame(song_item_recommendation, columns=['song_id', 'predicted_play_count'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7gewfmTATsnv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "de466007-ddf6-42d2-9d40-f363eb14d7a5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   song_id  play_freq  predicted_play_count  corrected_play_count\n",
              "4     2342        111              2.653903              2.558987\n",
              "2     5101        130              2.386577              2.298871\n",
              "3      139        119              2.313727              2.222057\n",
              "1     7519        168              2.270864              2.193712\n",
              "0     8099        275              2.212702              2.152399"
            ],
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              "  @keyframes spin {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"ranking_songs(song_item_recommendation, final_play)\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"song_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3391,\n        \"min\": 139,\n        \"max\": 8099,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5101,\n          8099,\n          139\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"play_freq\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 67,\n        \"min\": 111,\n        \"max\": 275,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          130,\n          275,\n          119\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_play_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1721796254152417,\n        \"min\": 2.2127017409877365,\n        \"max\": 2.653903109477311,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.386577162060947,\n          2.2127017409877365,\n          2.3137269182649587\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"corrected_play_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1621260817583845,\n        \"min\": 2.152399472072184,\n        \"max\": 2.558987309902061,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.298871360130244,\n          2.152399472072184,\n          2.2220570685621377\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "# Applying the ranking_songs function\n",
        "ranking_songs(song_item_recommendation, final_play)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ore9XTFgv5Np"
      },
      "source": [
        "**Observations and Insights:**\n",
        "* The **pearson_baseline similarity** metric has produced reasonable accurate predicted playcounts and the correction mechanism has adjusted the predictions to prevent overemphasis on highly played songs.\n",
        "* The overall ranking of songs based on the **predicted play count** is similar to the ranking based on **corrected play count**. This shows that the **corrections have not drastically changed the ranking** but have slightly reduced the predicted values acrossed all songs.\n",
        "* Songs with lower play frequencies, like 2343 maintain higher ranks despite the presence of more freqently played songs liek 8099, which promotes diversity in recommendation, allowing less popular but still highly predicted songs to remain near the top. This makes it more **robust against popularity biases**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rKgJpSA9vOOL"
      },
      "source": [
        "### Model Based Collaborative Filtering - Matrix Factorization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hJynidJCw-ti"
      },
      "source": [
        "Model-based Collaborative Filtering is a **personalized recommendation system**, the recommendations are based on the past behavior of the user and it is not dependent on any additional information. We use **latent features** to find recommendations for each user."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "07-2PT5Ssjqm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84059a76-cd09-4066-fc54-a83b98caaaa0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 1.0252\n",
            "Precision:  0.41\n",
            "Recall:  0.633\n",
            "F_1 score:  0.498\n"
          ]
        }
      ],
      "source": [
        "# Build baseline model using svd, we will use random_state = 1\n",
        "svd=SVD(random_state=1)\n",
        "\n",
        "# Train the algorithm on the trainset\n",
        "svd.fit(trainset)\n",
        "\n",
        "# Let us compute precision@k, recall@k, and f_1 score with k = 10\n",
        "precision_recall_at_k(svd)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yWIhfdxXsjqm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72eed718-eb21-4873-fa39-61f9078a47b0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user: 6958       item: 1671       r_ui = 2.00   est = 1.27   {'was_impossible': False}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Prediction(uid=6958, iid=1671, r_ui=2, est=1.267473397214638, details={'was_impossible': False})"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "# Making prediction for user (with user_id 6958) to song (with song_id 1671), take r_ui = 2\n",
        "svd.predict(uid=6958, iid=1671, r_ui = 2, verbose=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "APm-uMSvcAMf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f01b552-46df-4a1b-9027-4056f6b655e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user: 6958       item: 3232       r_ui = None   est = 1.56   {'was_impossible': False}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Prediction(uid=6958, iid=3232, r_ui=None, est=1.5561675084403663, details={'was_impossible': False})"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "# Making a prediction for the user who has not listened to the song (song_id 3232)\n",
        "svd.predict(uid=6958, iid=3232, verbose=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "> Although the model seemed more accurate, the predicted rating is lower than the previous model when compared to the real rating.\n"
      ],
      "metadata": {
        "id": "pgECFjzEUbHI"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "23tnRUJJxWTR"
      },
      "source": [
        "#### Improving matrix factorization based recommendation system by tuning its hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4bM81V_hvtwv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a99ae45d-ef88-418b-ff31-eef56db0c540"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best RMSE Score: 1.012\n",
            "Combination of parameters that gave the best RMSE score: {'n_epochs': 30, 'lr_all': 0.01, 'reg_all': 0.2}\n"
          ]
        }
      ],
      "source": [
        "# Set the parameter space to tune\n",
        "param_grid = {'n_epochs': [10, 20, 30], 'lr_all': [0.001, 0.005, 0.01],\n",
        "              'reg_all': [0.2, 0.4, 0.6]}\n",
        "\n",
        "# Performe 3-fold grid-search cross-validation\n",
        "gs = GridSearchCV(SVD, param_grid, measures=['rmse', 'mae'], cv=3, n_jobs=-1)\n",
        "\n",
        "# Fitting data\n",
        "gs.fit(data)\n",
        "\n",
        "# Best RMSE score\n",
        "print(f'Best RMSE Score: {round(gs.best_score[\"rmse\"],3)}')\n",
        "\n",
        "# Combination of parameters that gave the best RMSE score\n",
        "print(f'Combination of parameters that gave the best RMSE score: {gs.best_params[\"rmse\"]}')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aSgBRcL1xnVC"
      },
      "source": [
        "**Think About It**: How do the parameters affect the performance of the model? Can we improve the performance of the model further? Check the available hyperparameters [here](https://surprise.readthedocs.io/en/stable/matrix_factorization.html)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TA_7xe-nnhuu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e10e748-755c-411c-9d66-765366e038f0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 1.0141\n",
            "Precision:  0.415\n",
            "Recall:  0.635\n",
            "F_1 score:  0.502\n"
          ]
        }
      ],
      "source": [
        "# Building the optimized SVD model using optimal hyperparameters\n",
        "svd_algo_optimized = SVD(n_epochs=30, lr_all=0.01, reg_all=0.2, random_state=1)\n",
        "\n",
        "# Train the algorithm on the trainset\n",
        "svd_algo_optimized.fit(trainset)\n",
        "\n",
        "# Use the function precision_recall_at_k to compute precision, recall, F1 score and RMSE\n",
        "precision_recall_at_k(svd_algo_optimized)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l3t5JdBmxz8l"
      },
      "source": [
        "**Observations and Insights:**\n",
        "* Compared to the baseline model of SVD, **the recall (0.635)** is slightly better but not as good as the optimized item-item model.\n",
        "* The **RSME of 1.014** suggests that the optimized SVD model is more accurate than the baseline SVD.\n",
        "* The model suggests more relevant songs based on the **precision of 0.415**.\n",
        "* The **F1 score of 0.502 **indicates a finer balance and trade-off between precision and recall."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s6C1PAfboM8_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b35eeed5-af6d-41f0-9eff-d2be34209eda"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user: 6958       item: 1671       r_ui = 2.00   est = 1.34   {'was_impossible': False}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Prediction(uid=6958, iid=1671, r_ui=2, est=1.3432395286125098, details={'was_impossible': False})"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ],
      "source": [
        "# Using svd_algo_optimized model to recommend for userId 6958 and song_id 1671\n",
        "svd_algo_optimized.predict(uid=6958, iid=1671, r_ui = 2, verbose=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k1xjn3kOoQyg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3b99e43-8a72-4588-f23f-14f0a6343963"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user: 6958       item: 3232       r_ui = None   est = 1.44   {'was_impossible': False}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Prediction(uid=6958, iid=3232, r_ui=None, est=1.4425484461176483, details={'was_impossible': False})"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ],
      "source": [
        "# Using svd_algo_optimized model to recommend for userId 6958 and song_id 3232 with unknown baseline play_count\n",
        "svd_algo_optimized.predict(uid=6958, iid=3232, verbose=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qm732Wuvy76R"
      },
      "source": [
        "**Observations and Insights:**\n",
        "* Although the SVD optimized and SVD baseline model indicate better accuracy than the item-item and user-user model, the estimated prediction is still not as close to the real prediction."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1LGeE2EB_n90",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f8f8e95-7e9a-4376-ed7b-d1a302cb2435"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(7224, 2.601898709695318),\n",
              " (5653, 2.108727504933887),\n",
              " (8324, 2.014091144993034),\n",
              " (6450, 1.952492524218337),\n",
              " (9942, 1.9401146174388464)]"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ],
      "source": [
        "# Getting top 5 recommendations for user_id 6958 using \"svd_optimized\" algorithm\n",
        "svd_optimized_recommendations = get_recommendations(df_final, 6958, 5, svd_algo_optimized)\n",
        "svd_optimized_recommendations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6ngiGSJU818M",
        "colab": {
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          "height": 206
        },
        "outputId": "f659bf25-88a3-4a43-bf87-687dc9fa5e15"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   song_id  play_freq  predicted_play_count  corrected_play_count\n",
              "2     7224        107              2.601899              2.505225\n",
              "1     5653        108              2.108728              2.012502\n",
              "4     8324         96              2.014091              1.912029\n",
              "0     9942        150              1.940115              1.858465\n",
              "3     6450        102              1.952493              1.853478"
            ],
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              "variable_name": "svd_optimized_ranking",
              "summary": "{\n  \"name\": \"svd_optimized_ranking\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"song_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1675,\n        \"min\": 5653,\n        \"max\": 9942,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5653,\n          6450,\n          8324\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"play_freq\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21,\n        \"min\": 96,\n        \"max\": 150,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          108,\n          102,\n          96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_play_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2756481447211803,\n        \"min\": 1.9401146174388464,\n        \"max\": 2.601898709695318,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.108727504933887,\n          1.952492524218337,\n          2.014091144993034\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"corrected_play_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2741499360324433,\n        \"min\": 1.8534777699206697,\n        \"max\": 2.5052250607907514,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.0125024600689496,\n          1.8534777699206697,\n          1.9120290723770683\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "# Ranking songs based on above recommendations\n",
        "svd_optimized_ranking = ranking_songs(svd_optimized_recommendations, final_play)\n",
        "svd_optimized_ranking"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SepUU1Efy_9Z"
      },
      "source": [
        "**Observations and Insights:**\n",
        "* Song 7224 retains its top ranking, showing that it is **highly relevant even after correction**.\n",
        "* Songs with higher play frequency like 9942 are slightly penalized, but still remain relevant, indicating that the model **effectively balances recommendation diversity with acurracy**.\n",
        "* Overall, the SVD optimized model indicates **a well-distributed predictions**, and the correction mechanism fine-tunes these predictions to account for the song popularity, creating a more balanced user-tailored recommendation list."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "57b31de5"
      },
      "source": [
        "### Cluster Based Recommendation System"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Xv2AZCszCdN"
      },
      "source": [
        "In **clustering-based recommendation systems**, we explore the **similarities and differences** in people's tastes in songs based on how they rate different songs. We cluster similar users together and recommend songs to a user based on play_counts from other users in the same cluster."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0c4b20e4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ff803da-aef8-40db-bcbf-31de1cda8aad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 1.0487\n",
            "Precision:  0.397\n",
            "Recall:  0.582\n",
            "F_1 score:  0.472\n"
          ]
        }
      ],
      "source": [
        "# Make baseline clustering model\n",
        "cluster_baseline = CoClustering(random_state=1)\n",
        "\n",
        "# Train the algorithm on the trainset\n",
        "cluster_baseline.fit(trainset)\n",
        "\n",
        "# Let us compute precision@k, recall@k, and f_1 score with k = 10\n",
        "precision_recall_at_k(cluster_baseline)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "11dbdc0f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ed7f2cd-3878-46c4-9530-8d11e2d72aad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user: 6958       item: 1671       r_ui = 2.00   est = 1.29   {'was_impossible': False}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Prediction(uid=6958, iid=1671, r_ui=2, est=1.2941824757363074, details={'was_impossible': False})"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "# Making prediction for user_id 6958 and song_id 1671\n",
        "cluster_baseline.predict(uid=6958, iid=1671, r_ui = 2, verbose=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dab1aaed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6055d9e7-c737-4624-9326-3619bd10565e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user: 6958       item: 3232       r_ui = None   est = 1.48   {'was_impossible': False}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Prediction(uid=6958, iid=3232, r_ui=None, est=1.4785259100797417, details={'was_impossible': False})"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ],
      "source": [
        "# Making prediction for user (userid 6958) for a song(song_id 3232) not listened to by the user\n",
        "cluster_baseline.predict(uid=6958, iid=3232, verbose=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c2fd66f5"
      },
      "source": [
        "#### Improving clustering-based recommendation system by tuning its hyper-parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "efe7d8e6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3428a83a-dd9d-4547-d611-bcf04f3c7ac3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best RMSE Score: 1.046\n",
            "Combination of parameters that gave the best RMSE score: {'n_cltr_u': 3, 'n_cltr_i': 3, 'n_epochs': 30}\n"
          ]
        }
      ],
      "source": [
        "# Set the parameter space to tune\n",
        "param_grid = {'n_cltr_u': [3, 4, 5, 6], 'n_cltr_i': [3, 4, 5, 6], 'n_epochs': [30, 40, 50]}\n",
        "\n",
        "# Performing 3-fold grid search cross-validation\n",
        "cl_gs = GridSearchCV(CoClustering, param_grid, measures=['rmse', 'mae'], cv=3, n_jobs=-1)\n",
        "\n",
        "# Fitting data\n",
        "cl_gs.fit(data)\n",
        "\n",
        "# Best RMSE score\n",
        "print(f'Best RMSE Score: {round(cl_gs.best_score[\"rmse\"],3)}')\n",
        "# Combination of parameters that gave the best RMSE score\n",
        "print(f'Combination of parameters that gave the best RMSE score: {cl_gs.best_params[\"rmse\"]}')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CS6aMVJLyj21"
      },
      "source": [
        "**Think About It**: How do the parameters affect the performance of the model? Can we improve the performance of the model further? Check the available hyperparameters [here](https://surprise.readthedocs.io/en/stable/co_clustering.html)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5a7a8a30",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b057d22b-60e8-4751-b7ac-6ff1dc64c660"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 1.0487\n",
            "Precision:  0.397\n",
            "Recall:  0.582\n",
            "F_1 score:  0.472\n"
          ]
        }
      ],
      "source": [
        "# Applying the tuned values Coclustering algorithm\n",
        "cluster_tuned = CoClustering(n_cltr_u=3, n_cltr_i=3, n_epochs=40, random_state=1)\n",
        "\n",
        "# Train the algorithm on the trainset\n",
        "cluster_tuned.fit(trainset)\n",
        "\n",
        "# Compute precision@k to evaluate\n",
        "precision_recall_at_k(cluster_tuned)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l-Jvce1gznKa"
      },
      "source": [
        "**Observations and Insights:**\n",
        "* There is **no change** in metrics after tuning.\n",
        "* The **hyperparameter settings may have been ineffective**: the tuning process might not have significantly altered the clustering model's hyperparameters.\n",
        "* **Clustering method limitation**: it is possible that the cluster formed by the baseline model were already quite optimal.\n",
        "* **Inherent limits of the model**: the performance may be constrained by the inherent structure of the clustering-based approach, further tuning may not be enough to enhance the performance.\n",
        "\n",
        "- We can consider **exploring alternative models** to see if the performance is better.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6ba5b26b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "633c8b49-fabd-4884-c836-9214bdb2dc7c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user: 6958       item: 1671       r_ui = 2.00   est = 1.29   {'was_impossible': False}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Prediction(uid=6958, iid=1671, r_ui=2, est=1.2941824757363074, details={'was_impossible': False})"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ],
      "source": [
        "# Using co_clustering_optimized model to recommend for userId 6958 and song_id 1671\n",
        "cluster_tuned.predict(uid=6958, iid=1671, r_ui = 2, verbose=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ec582940",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "56f73474-379d-46e0-9805-77b640c7db39"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user: 6958       item: 3232       r_ui = None   est = 1.48   {'was_impossible': False}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Prediction(uid=6958, iid=3232, r_ui=None, est=1.4785259100797417, details={'was_impossible': False})"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ],
      "source": [
        "# Use Co_clustering based optimized model to recommend for userId 6958 and song_id 3232 with unknown baseline play_count\n",
        "cluster_tuned.predict(uid=6958, iid=3232, verbose=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rjGUSMqrzoDH"
      },
      "source": [
        "**Observations and Insights:**\n",
        "* The **model underestimated** the user's preference for song 1671. The actual play count is 2.00, while the estimation is 1.29\n",
        "* The difference between the actual rating and estimated rating indicates that the model is **struggling to accurately capture** the user's true preference for this specific song.\n",
        "* The **estimation is relatively low** for both items indicating that system might be too conservative in predicting higher ratings or may not be clustering similar songs/users in a way that matches the user's behavior."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "df9e28ba"
      },
      "source": [
        "#### Implementing the recommendation algorithm based on optimized CoClustering model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e0f36e15",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99882356-b97e-49e4-cf72-38d0ffda5f6e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(7224, 3.0947973198294125),\n",
              " (8324, 2.3114984930523246),\n",
              " (9942, 2.2150393965932285),\n",
              " (5531, 2.1245632061170374),\n",
              " (4831, 2.1237828270757895)]"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ],
      "source": [
        "# Getting top 5 recommendations for user_id 6958 using \"Co-clustering based optimized\" algorithm\n",
        "co_cluster_tuned_recommendations = get_recommendations(df_final, 6958, 5, cluster_tuned)\n",
        "co_cluster_tuned_recommendations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f1696941"
      },
      "source": [
        "### Correcting the play_count and Ranking the above songs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c186f13b",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "55708cb1-c651-4274-9114-885f346f362b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   song_id  play_freq  predicted_play_count  corrected_play_count\n",
              "2     7224        107              3.094797              2.998124\n",
              "4     8324         96              2.311498              2.209436\n",
              "1     9942        150              2.215039              2.133390\n",
              "0     5531        618              2.124563              2.084337\n",
              "3     4831         97              2.123783              2.022248"
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              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ae537850-7a1d-472b-99d2-ecf75fbb4f99 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
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              "\n",
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              "    <style>\n",
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              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
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              "        width: 32px;\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('co_cluster_tuned_ranking')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "        document.querySelector('#id_0bb04cd7-77fa-472a-9a01-cef7cdafa07b button.colab-df-generate');\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('co_cluster_tuned_ranking');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "co_cluster_tuned_ranking",
              "summary": "{\n  \"name\": \"co_cluster_tuned_ranking\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"song_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2072,\n        \"min\": 4831,\n        \"max\": 9942,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          8324,\n          4831,\n          9942\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"play_freq\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 227,\n        \"min\": 96,\n        \"max\": 618,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          96,\n          97,\n          150\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_play_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.41035076932028164,\n        \"min\": 2.1237828270757895,\n        \"max\": 3.0947973198294125,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.3114984930523246,\n          2.1237828270757895,\n          2.2150393965932285\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"corrected_play_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4020085033022742,\n        \"min\": 2.0222482105624278,\n        \"max\": 2.998123670924846,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.209436420436359,\n          2.0222482105624278,\n          2.1333897385004557\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 86
        }
      ],
      "source": [
        "# Ranking songs based on the above recommendations\n",
        "co_cluster_tuned_ranking = ranking_songs(co_cluster_tuned_recommendations, final_play)\n",
        "co_cluster_tuned_ranking"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_uJ_nZjBzvKH"
      },
      "source": [
        "**Observations and Insights:**\n",
        "* **Song 7224 remains the top recommendation**, with the highest predicted and corrected play counts, indicating that it is the most relevant song for user, despite not having the highest play frequency.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5U56oSNsR-F2"
      },
      "source": [
        "### Content Based Recommendation Systems"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9aTEqaOjhoEg"
      },
      "source": [
        "**Think About It:** So far we have only used the play_count of songs to find recommendations but we have other information/features on songs as well. Can we take those song features into account?"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_small = df_final"
      ],
      "metadata": {
        "id": "sFlR83xyjaVo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UX826CsjR-F3"
      },
      "outputs": [],
      "source": [
        "# Concatenate the \"title\", \"release\", \"artist_name\" columns to create a different column named \"text\"\n",
        "df_small['text'] = df_small['title'] + ' ' + df_small['release'] + ' ' + df_small['artist_name']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WdXw4U-wR-F4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "b6b68ee4-9387-4e8a-b210-24ddb5de5191"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 user_id  song_id  play_count  \\\n",
              "title                                                           \n",
              "Daisy And Prudence                  6958      447           1   \n",
              "The Ballad of Michael Valentine     6958      512           1   \n",
              "I Stand Corrected (Album)           6958      549           1   \n",
              "They Might Follow You               6958      703           1   \n",
              "Monkey Man                          6958      719           1   \n",
              "\n",
              "                                                                              text  \n",
              "title                                                                               \n",
              "Daisy And Prudence                    Daisy And Prudence Distillation Erin McKeown  \n",
              "The Ballad of Michael Valentine  The Ballad of Michael Valentine Sawdust The Ki...  \n",
              "I Stand Corrected (Album)        I Stand Corrected (Album) Vampire Weekend Vamp...  \n",
              "They Might Follow You                They Might Follow You Tiny Vipers Tiny Vipers  \n",
              "Monkey Man                           Monkey Man You Know I'm No Good Amy Winehouse  "
            ],
            "text/html": [
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              "    <div>\n",
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              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_small",
              "summary": "{\n  \"name\": \"df_small\",\n  \"rows\": 561,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 561,\n        \"samples\": [\n          \"Sweet home Alabama\",\n          \"I'm Done\",\n          \"Seven Nation Army\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"user_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18353,\n        \"min\": 11,\n        \"max\": 75901,\n        \"num_unique_values\": 68,\n        \"samples\": [\n          27729,\n          67704,\n          49549\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"song_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2898,\n        \"min\": 21,\n        \"max\": 9989,\n        \"num_unique_values\": 561,\n        \"samples\": [\n          8411,\n          8256,\n          310\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"play_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5,\n          4,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 561,\n        \"samples\": [\n          \"Sweet home Alabama To Die For Lynyrd Skynyrd\",\n          \"I'm Done Doll Domination The Pussycat Dolls\",\n          \"Seven Nation Army Seven Nation Army The White Stripes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 89
        }
      ],
      "source": [
        "# Select the columns 'user_id', 'song_id', 'play_count', 'title', 'text' from df_small data\n",
        "df_small = df_small[['user_id', 'song_id', 'play_count', 'title', 'text']]\n",
        "\n",
        "# Drop the duplicates from the title column\n",
        "df_small.drop_duplicates(subset=['title'], inplace=True)\n",
        "\n",
        "# Set the title column as the index\n",
        "df_small.set_index('title', inplace=True)\n",
        "\n",
        "# See the first 5 records of the df_small dataset\n",
        "df_small.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qDcYHwZTR-F5"
      },
      "outputs": [],
      "source": [
        "# Create the series of indices from the data\n",
        "indices = pd.Series(df_small.index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9UINF3Nwvwfr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b10dc7ee-f621-4fb4-ae0f-b932a9674867"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        }
      ],
      "source": [
        "# Importing necessary packages to work with text data\n",
        "import nltk\n",
        "\n",
        "# Download punkt library\n",
        "nltk.download('punkt')\n",
        "\n",
        "# Download stopwords library\n",
        "nltk.download('stopwords')\n",
        "\n",
        "# Download wordnet\n",
        "nltk.download('wordnet')\n",
        "\n",
        "# Import regular expression\n",
        "import re\n",
        "\n",
        "# Import word_tokenizer\n",
        "from nltk.tokenize import word_tokenize\n",
        "\n",
        "# Import WordNetLemmatizer\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "# Import stopwords\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "# Import CountVectorizer and TfidfVectorizer\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jt2vitlnhoEg"
      },
      "source": [
        "We will create a **function to pre-process the text data:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j5QSSeUvR-F6"
      },
      "outputs": [],
      "source": [
        "# Create a function to tokenize the text\n",
        "def tokenize(text):\n",
        "\n",
        "  # Make each letter as lowercase and removing non-alphabetical rext\n",
        "  text = re.sub('[^a-zA-Z]', ' ', text.lower())\n",
        "\n",
        "  # Tokenize the text\n",
        "  tokens = word_tokenize(text)\n",
        "\n",
        "  # Remove the stopwords\n",
        "  words = [word for word in tokens if word not in stopwords.words('english')]\n",
        "\n",
        "  # Lemmatize the words\n",
        "  text_lems = [WordNetLemmatizer().lemmatize(word) for word in words]\n",
        "\n",
        "  return text_lems"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RI_onIGdR-F6"
      },
      "outputs": [],
      "source": [
        "# Create tfidf vectorizer\n",
        "tfidf = TfidfVectorizer(tokenizer=tokenize)\n",
        "\n",
        "# Fit_transfrom the above vectorizer on the text column and then convert the output into an array\n",
        "song_tfidf = tfidf.fit_transform(df_small['text']).toarray()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make the dataframe of song_tfidf data\n",
        "pd.DataFrame(song_tfidf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "OJoep-OQk_mI",
        "outputId": "63cf92e1-aaca-4964-e125-a07048537107"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
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          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have extracted features from the text data. Now, we can find similarities between songs using these features. We will use **cosine similarity** to calculate the similarity."
      ],
      "metadata": {
        "id": "klZUnJg7lQcK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Beak6ODRR-F7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3d4ddfa-395a-44af-80ef-3170d171c90c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 1., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 1., ..., 0., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., ..., 1., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 1., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ],
      "source": [
        "# Compute the cosine similarity for the tfidf above output\n",
        "similar_songs = cosine_similarity(song_tfidf)\n",
        "\n",
        "similar_songs"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observation:\n",
        "- 1 is diagonal and a lot of 0s. This shows that the cosine similarity is quite sparse showing limited diversity."
      ],
      "metadata": {
        "id": "dK4mVomE8y6S"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Jjo3UHKhoEh"
      },
      "source": [
        " Finally, let's create a function to find most similar songs to recommend for a given song."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "upANOISkR-F8"
      },
      "outputs": [],
      "source": [
        "# Function that takes in song title as input and returns the top 10 recommended songs\n",
        "def recommendations(title, similar_songs):\n",
        "    recommended_songs = []\n",
        "\n",
        "\n",
        "    # Getting the index of the song that matches the title\n",
        "    idx = indices[indices == title].index[0]\n",
        "\n",
        "    # Creating a Series with the similarity scores in descending order\n",
        "    score_series = pd.Series(similar_songs[idx]).sort_values(ascending = False)\n",
        "\n",
        "    # Getting the indexes of the 10 most similar songs\n",
        "    top_10_indexes = list(score_series.iloc[1:11].index)\n",
        "\n",
        "    # Populating the list with the titles of the best 10 matching songs\n",
        "    for i in top_10_indexes:\n",
        "        recommended_songs.append(list(df_small.index)[i])\n",
        "\n",
        "    return recommended_songs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o4EINBmkR-F8"
      },
      "source": [
        "Recommending 10 songs similar to Learn to Fly"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ohEK5dkVR-F8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e5c2b3c-afef-4bc5-9680-28a7b6675ed0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Everlong',\n",
              " 'The Pretender',\n",
              " 'Nothing Better (Album)',\n",
              " 'From Left To Right',\n",
              " 'Lifespan Of A Fly',\n",
              " 'Under The Gun',\n",
              " 'I Need A Dollar',\n",
              " 'Feel The Love',\n",
              " 'All The Pretty Faces',\n",
              " 'Bones']"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ],
      "source": [
        "# Make the recommendation for the song with title 'Learn To Fly'\n",
        "recommendations('Learn To Fly', similar_songs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UQ7iI5QJ0oem"
      },
      "source": [
        "**Observations and Insights:**\n",
        "* The recommendation seems to include a **sparse cosine similarity matrix**, showing limited diversity.\n",
        "* Songs like **Everlong** or **Pretender** suggest strong connection to the **Foo Fighters** the same band that performs **Learn To Fly**. This can indicate a recommendation system considers **artists similarity overlap**.\n",
        "* Overall, the recommendations appear to offer a **mix of high-energy rock songs** with some diverse options, it could be that the **lack of meaningful similarities** in the sparse matrix leads to inconsistent results."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Consolidating all performance:**\n",
        "\n",
        "\n",
        "---\n",
        "| <h3>Technique</h3> | <h3>RMSE</h3> | <h3>Precision</h3> | <h3>Recall</h3> | <h3>F1 Score</h3> |\n",
        "|---------|----|---------|------|---------|\n",
        "|<h4>User-user similarity</h4>|<h4>1.0878|<h4>0.396|<h4>0.629|<h4>0.504|\n",
        "|<h4>User-user Optimized</h4>|<h4>1.0328|<h4>0.408|<h4>0.665|<h4>0.506|\n",
        "|<h4>Item-item similarity</h4>|<h4>1.0394| <h4>0.307|<h4>0.562|<h4>0.397|\n",
        "|<h4>Item-item Optimized</h4>|<h4>1.0328| <h4>0.408|<h4>0.665|<h4>0.506|\n",
        "|<h4>SVD similarity matrix</h4>|<h4>1.0252| <h4>0.41|<h4>0.633| <h4>0.498|\n",
        "|-----------------------------------------------------------------------|--|--|--|--|--|\n",
        "|<h4>SVD Optimized **</h4>|<h4>1.0141| <h4>0.415|<h4>0.635| <h4>0.502|\n",
        "|-----------------------------------------------------------------------|--|--|--|--|--|\n",
        "|<h4>Cluster-based recommendaiton</h4>|<h4>1.0487|<h4> 0.397|<h4>0.582| <h4>0.472|\n",
        "|<h4>Cluster-based Optimized</h4>|<h4>1.0487| <h4>0.397|<h4>0.582| <h4>0.472|\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "Content-based with cosine similarity\n",
        "\n",
        "```\n",
        "[1., 0., 0., ..., 0., 0., 0.],\n",
        "[0., 1., 0., ..., 0., 0., 0.],\n",
        "[0., 0., 1., ..., 0., 0., 0.],\n",
        "       ...,\n",
        "[0., 0., 0., ..., 1., 0., 0.],\n",
        "[0., 0., 0., ..., 0., 1., 0.],\n",
        "[0., 0., 0., ..., 0., 0., 1.]]\n",
        "```\n",
        "\n",
        "\n",
        "\n"
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        "id": "-MhJR3zWvZog"
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