diff --git a/Music recommendation feature.ipynb b/Music recommendation feature.ipynb
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+{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":8726191,"sourceType":"datasetVersion","datasetId":5236926}],"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Content Based Recommendation Systems\n\nA recommendation system (or recommender system) is a class of machine learning that uses data to help predict, narrow down, and find what people are looking for among an exponentially growing number of options.\n\nRecommendation systems are divided into three:\n\n* Collaborative Filtering\n* Content Based RS\n* Hybrid Models\n\nIn this notebook we are going to discuss Content Based RS.\n\n## Content Based Recommendation Systems\n\n* Content-based filtering methods are based on a description of the item and a profile of the user's preferences. These methods are best suited to situations where there is known data on an item (name, location, description, etc.), but not on the user. Content-based recommenders treat recommendation as a user-specific classification problem and learn a classifier for the user's likes and dislikes based on an item's features.\n* It is used to models such as TF_IDF and Word2Vec in order to capture similarity.\n* It is very powerful that a item adding newly is recommend.  \n* A key issue with content-based filtering is whether the system can learn user preferences from users' actions regarding one content source and use them across other content types. When the system is limited to recommending content of the same type as the user is already using, the value from the recommendation system is significantly less than when other content types from other services can be recommended.\n* To overcome this, most content-based recommender systems now use some form of the hybrid system.\n* Content-based recommender systems can also include opinion-based recommender systems. ","metadata":{"_uuid":"211893b3-b34f-45c2-8bba-ae96b890ff26","_cell_guid":"64ffe843-0f51-4bf5-9d01-3479b120064d","trusted":true}},{"cell_type":"markdown","source":"## What is TF-IDF?\n\nTF-IDF stands for Term Frequency Inverse Document Frequency of records. It can be defined as the calculation of how relevant a word in a series or corpus is to a text. The meaning increases proportionally to the number of times in the text a word appears but is compensated by the word frequency in the corpus (data-set).\n\nTF-IDF is a weight factor which a word display important into document and had been calculated with statistics method. TF-IDF method use to a lot domains (sentiment analysis, RS, stop words filterin etc.) This method is divided into two. Fistly we will analyze Term Frequency(TF).\n\n### Term Frequency\n\nIn document d, the frequency represents the number of instances of a given word t. Therefore, we can see that it becomes more relevant when a word appears in the text, which is rational. Since the ordering of terms is not significant, we can use a vector to describe the text in the bag of term models. For each specific term in the paper, there is an entry with the value being the term frequency.\nThe weight of a term that occurs in a document is simply proportional to the term frequency.\n\n### Inverse Document Frequency\n\nMainly, it tests how relevant the word is. The key aim of the search is to locate the appropriate records that fit the demand. Since tf considers all terms equally significant, it is therefore not only possible to use the term frequencies to measure the weight of the term in the paper. First, find the document frequency of a term t by counting the number of documents containing the term:\n\n**TF-IDF method used as multipy TF value and IDF value. (TF * IDF)**\n\nI have applied this method in my model. And I have found the similarity in between with cosine distance.","metadata":{"_uuid":"ef5594de-1f51-4f29-a84f-19136909b11e","_cell_guid":"87fdf135-277f-4fce-af94-b69dcb9ff45e","trusted":true}},{"cell_type":"markdown","source":"","metadata":{"_uuid":"2f2f516a-e4cc-4223-94d7-9e373614f9ef","_cell_guid":"2db1677c-31ac-4739-bb17-934203a8c590","trusted":true}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# For Text\n\nimport matplotlib.pyplot as plt\nimport seaborn as sb\n\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.manifold import TSNE\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\n\n# Capture similarity \nfrom sklearn.metrics.pairwise import linear_kernel\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"969f5946-9174-4282-a6ad-d3735e6d38ae","_cell_guid":"c91fd311-1267-464c-9f00-6d899c98ac9a","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-07-18T06:43:35.099082Z","iopub.execute_input":"2024-07-18T06:43:35.099880Z","iopub.status.idle":"2024-07-18T06:43:37.958493Z","shell.execute_reply.started":"2024-07-18T06:43:35.099838Z","shell.execute_reply":"2024-07-18T06:43:37.957125Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's get to know our dataset.","metadata":{"_uuid":"f815a9d3-982d-4783-8ff2-a97788b32b76","_cell_guid":"d0583ba6-7426-4f13-b439-4f1a1a4f063c","trusted":true}},{"cell_type":"code","source":"\n\ndata=pd.read_csv(\"/kaggle/input/musicaldata/musicaldata.csv\")\ndata.head(4000)","metadata":{"_uuid":"7835eb94-3a34-4253-a5d9-5ed64b45283d","_cell_guid":"0ff8ee63-f975-46ed-8346-450fef2d00f1","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-07-18T06:43:37.960867Z","iopub.execute_input":"2024-07-18T06:43:37.961541Z","iopub.status.idle":"2024-07-18T06:43:38.037706Z","shell.execute_reply.started":"2024-07-18T06:43:37.961497Z","shell.execute_reply":"2024-07-18T06:43:38.036554Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"> I wanted to make a suggestion depends on the cast members, description and \"listed_in\" which contains the shows type but there is another column named \"type\" it is a bit confusing I know. >","metadata":{"_uuid":"f47c0286-051f-44b7-8f3b-dff2636c57a2","_cell_guid":"5e4f0150-982c-40c2-ae42-df0e5b50ca14","trusted":true}},{"cell_type":"markdown","source":"Drop nan values on these columns to make a proper matrix which contains linear_kernel values of selected strings.","metadata":{"_uuid":"9e742cf3-6450-474e-b320-42275dea7b1b","_cell_guid":"4d9511c6-3b3c-4a66-b1f8-b77c1eb16472","trusted":true}},{"cell_type":"code","source":"data.shape","metadata":{"_uuid":"86c52013-2fe9-4582-826e-83e63d83a71c","_cell_guid":"546f44f4-1e9f-4316-b67a-4147d1cc61ac","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-07-18T06:43:38.039377Z","iopub.execute_input":"2024-07-18T06:43:38.040377Z","iopub.status.idle":"2024-07-18T06:43:38.048032Z","shell.execute_reply.started":"2024-07-18T06:43:38.040335Z","shell.execute_reply":"2024-07-18T06:43:38.046782Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.info()","metadata":{"_uuid":"c20641aa-17af-4e43-ab22-671baaf3af06","_cell_guid":"5ccfef9a-1c31-4211-977d-71895d623c2e","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-07-18T06:43:38.049427Z","iopub.execute_input":"2024-07-18T06:43:38.050029Z","iopub.status.idle":"2024-07-18T06:43:38.081228Z","shell.execute_reply.started":"2024-07-18T06:43:38.049999Z","shell.execute_reply":"2024-07-18T06:43:38.079843Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.isnull().sum()","metadata":{"_uuid":"488e4a21-2e98-4b64-b4b7-1f3a8774d197","_cell_guid":"8782b655-413f-407e-b6b0-95f80f0c86c4","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-07-18T06:43:38.085155Z","iopub.execute_input":"2024-07-18T06:43:38.085578Z","iopub.status.idle":"2024-07-18T06:43:38.097471Z","shell.execute_reply.started":"2024-07-18T06:43:38.085546Z","shell.execute_reply":"2024-07-18T06:43:38.096046Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.dropna(inplace = True)\ndata.isnull().sum().plot.bar()\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-07-18T06:43:38.098926Z","iopub.execute_input":"2024-07-18T06:43:38.099358Z","iopub.status.idle":"2024-07-18T06:43:38.469804Z","shell.execute_reply.started":"2024-07-18T06:43:38.099322Z","shell.execute_reply":"2024-07-18T06:43:38.468707Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = data.drop(['track id'], axis = 1)\n\n\ndata","metadata":{"_uuid":"705daa1b-1a41-4765-a5c4-ca06243f8e73","_cell_guid":"9587614f-971d-4d90-a243-7059d10e7a53","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-07-18T06:43:38.471285Z","iopub.execute_input":"2024-07-18T06:43:38.472181Z","iopub.status.idle":"2024-07-18T06:43:38.503136Z","shell.execute_reply.started":"2024-07-18T06:43:38.472139Z","shell.execute_reply":"2024-07-18T06:43:38.501725Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = data.sort_values(by=[' mood'], ascending=False)\ndata\n","metadata":{"_uuid":"7d10088e-9fb4-4e32-8a52-f0355a04e139","_cell_guid":"54bd07ed-5919-4c34-b8c2-537fc8bb4c63","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-07-18T06:43:38.504752Z","iopub.execute_input":"2024-07-18T06:43:38.505194Z","iopub.status.idle":"2024-07-18T06:43:38.531330Z","shell.execute_reply.started":"2024-07-18T06:43:38.505155Z","shell.execute_reply":"2024-07-18T06:43:38.530232Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%capture\nsong_vectorizer = CountVectorizer(lowercase=False)\nsong_vectorizer.fit(data[' genre'])\n","metadata":{"_uuid":"f4e99d39-6244-4a54-bd59-da0c0b4eb263","_cell_guid":"4cc6a4dd-69da-4dd2-9e9f-3db4ad1bf9ec","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-07-18T07:20:14.961494Z","iopub.execute_input":"2024-07-18T07:20:14.962320Z","iopub.status.idle":"2024-07-18T07:20:15.016863Z","shell.execute_reply.started":"2024-07-18T07:20:14.962281Z","shell.execute_reply":"2024-07-18T07:20:15.015952Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"_uuid":"27044823-561d-4684-9c1e-b58330477a09","_cell_guid":"b02809b0-533d-4f2a-a814-bcb371d4b623","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = data.sort_values(by=[' mood'], ascending=False).head(8407)\n","metadata":{"_uuid":"9b9c0d05-34c7-4fbe-b3e8-3da5311f79aa","_cell_guid":"0895c987-d693-4ec5-98f4-52964893929f","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-07-18T07:20:18.625596Z","iopub.execute_input":"2024-07-18T07:20:18.626296Z","iopub.status.idle":"2024-07-18T07:20:18.633823Z","shell.execute_reply.started":"2024-07-18T07:20:18.626259Z","shell.execute_reply":"2024-07-18T07:20:18.632754Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def get_similarities(song_genre, datas):\n\n# Getting vector for the input song.\n    text_array1 = song_vectorizer.transform(datas[datas[' genre']==song_genre][' mother tongue']).toarray()\n    num_array1 = datas[datas[' genre']==song_genre].select_dtypes(include=np.number).to_numpy()\n\n# We will store similarity for each row of the dataset.\n    sim = []\n    for idx, row in data.iterrows():\n\t    genre = row[' genre']\n\t\n\t# Getting vector for current song.\n\t    text_array2 = song_vectorizer.transform(datas[datas[' genre']==genre][' mother tongue']).toarray()\n\t    num_array2 = datas[datas[' genre']==genre].select_dtypes(include=np.number).to_numpy()\n\n\t# Calculating similarities for text as well as numeric features\n\t    text_sim = cosine_similarity(text_array1, text_array2)[0][0]\n\t    num_sim = cosine_similarity(num_array1, num_array2)[0][0]\n\t    sim.append(text_sim + num_sim)\n\t\n    return sim\n","metadata":{"_uuid":"7f64a3c5-6644-4500-b768-9588a1a1796e","_cell_guid":"f6ba9b5c-1cd6-4329-9716-768fc66b6185","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-07-18T07:21:22.321219Z","iopub.execute_input":"2024-07-18T07:21:22.322530Z","iopub.status.idle":"2024-07-18T07:21:22.332061Z","shell.execute_reply.started":"2024-07-18T07:21:22.322483Z","shell.execute_reply":"2024-07-18T07:21:22.330729Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def recommend_songs(song_genre, datas=data):\n  # Base case\n   if data[data[' genre'] == song_genre].shape[0] == 0:\n        print('This song is not so popular')\n     \n        for song in datas.sample(n=5)[' genre'].values:\n            print(song)\n        return\n   \n   datas['similarity_factor'] = get_similarities(song_genre, datas)\n \n   datas.sort_values(by=['similarity_factor', ' mood'],\n                   ascending = [False, False],\n                   inplace=True)\n   \n  # First song will be the input song itself as the similarity will be highest.\n   display(datas[[' genre', ' age', ' mother tongue']][2:7])\n","metadata":{"_uuid":"f42b9e20-bfde-468c-a061-baede0e7acc9","_cell_guid":"16949b27-8cac-4a55-84f8-c0109e8141f5","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-07-18T07:32:45.646510Z","iopub.execute_input":"2024-07-18T07:32:45.646931Z","iopub.status.idle":"2024-07-18T07:32:45.656010Z","shell.execute_reply.started":"2024-07-18T07:32:45.646887Z","shell.execute_reply":"2024-07-18T07:32:45.654684Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"recommend_songs('classical')\n","metadata":{"_uuid":"123ddf07-1178-4198-82ac-3e4b0077b682","_cell_guid":"5c7396f0-7d3a-48f5-8c60-9dc8a74d671f","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-07-18T07:50:20.350621Z","iopub.execute_input":"2024-07-18T07:50:20.351890Z","iopub.status.idle":"2024-07-18T07:59:03.032540Z","shell.execute_reply.started":"2024-07-18T07:50:20.351829Z","shell.execute_reply":"2024-07-18T07:59:03.031273Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"_uuid":"8fc6e3b5-9204-4b61-81f1-1ad298187b0b","_cell_guid":"0d8ef6be-f82b-4727-bf71-ae5e3b93b065","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"_uuid":"db38054b-03da-4b74-90c8-da0952e22e97","_cell_guid":"00755ea1-9ccb-48c3-b5bf-ed8502dba41a","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"_uuid":"ebeabf14-181a-4931-a489-3692eaa20b94","_cell_guid":"2c060445-e4a7-47f3-bc6e-77b60ca5f21d","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"_uuid":"fd7cc031-07e1-470a-b04e-e4914737aa34","_cell_guid":"f34ee388-4084-468f-bce4-f85059694cda","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n","metadata":{"_uuid":"8fe27341-f205-43b1-bbb0-1aeac550fa46","_cell_guid":"e6249211-42c8-4197-ab2d-cedf58afcd17","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"In this above. My model brought the recommendations I wanted to bring acording to the mood of the songs of different genres . Firstly it brought to Transformers movies.\n\n# Conclusion\n\n* In this notebook. I worked to explain content based recommendation system.\n* Content based RS models are powerful in new item recommendation. \n* In general it is used TF-IDF and Word2Vec model while content based RS designs.\n* TF-IDF method is the method which explains words frequency in document.\n* It can be created a recommendation model by using cosine distance with the aid of TF-IDF weights.\n\n","metadata":{"_uuid":"a5fbdfc2-4e81-4d76-985e-c10441e7fe32","_cell_guid":"8d31b41f-78cd-4b03-b692-8644e3d9a1be","trusted":true}}]}
diff --git a/notebook10298254f6.ipynb b/notebook10298254f6.ipynb
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+++ b/notebook10298254f6.ipynb
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+{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":8726191,"sourceType":"datasetVersion","datasetId":5236926}],"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Content Based Recommendation Systems\n\nA recommendation system (or recommender system) is a class of machine learning that uses data to help predict, narrow down, and find what people are looking for among an exponentially growing number of options.\n\nRecommendation systems are divided into three:\n\n* Collaborative Filtering\n* Content Based RS\n* Hybrid Models\n\nIn this notebook we are going to discuss Content Based RS.\n\n## Content Based Recommendation Systems\n\n* Content-based filtering methods are based on a description of the item and a profile of the user's preferences. These methods are best suited to situations where there is known data on an item (name, location, description, etc.), but not on the user. Content-based recommenders treat recommendation as a user-specific classification problem and learn a classifier for the user's likes and dislikes based on an item's features.\n* It is used to models such as TF_IDF and Word2Vec in order to capture similarity.\n* It is very powerful that a item adding newly is recommend.  \n* A key issue with content-based filtering is whether the system can learn user preferences from users' actions regarding one content source and use them across other content types. When the system is limited to recommending content of the same type as the user is already using, the value from the recommendation system is significantly less than when other content types from other services can be recommended.\n* To overcome this, most content-based recommender systems now use some form of the hybrid system.\n* Content-based recommender systems can also include opinion-based recommender systems.","metadata":{"_uuid":"cca15c56-4f26-4e9a-b4a8-2eca1c5ab108","_cell_guid":"d594d373-a86f-489c-87d1-45302b1b86a3","trusted":true}},{"cell_type":"markdown","source":"## What is TF-IDF?\n\nTF-IDF stands for Term Frequency Inverse Document Frequency of records. It can be defined as the calculation of how relevant a word in a series or corpus is to a text. The meaning increases proportionally to the number of times in the text a word appears but is compensated by the word frequency in the corpus (data-set).\n\nTF-IDF is a weight factor which a word display important into document and had been calculated with statistics method. TF-IDF method use to a lot domains (sentiment analysis, RS, stop words filterin etc.) This method is divided into two. Fistly we will analyze Term Frequency(TF).\n\n### Term Frequency\n\nIn document d, the frequency represents the number of instances of a given word t. Therefore, we can see that it becomes more relevant when a word appears in the text, which is rational. Since the ordering of terms is not significant, we can use a vector to describe the text in the bag of term models. For each specific term in the paper, there is an entry with the value being the term frequency.\nThe weight of a term that occurs in a document is simply proportional to the term frequency.\n\n### Inverse Document Frequency\n\nMainly, it tests how relevant the word is. The key aim of the search is to locate the appropriate records that fit the demand. Since tf considers all terms equally significant, it is therefore not only possible to use the term frequencies to measure the weight of the term in the paper. First, find the document frequency of a term t by counting the number of documents containing the term:\n\n**TF-IDF method used as multipy TF value and IDF value. (TF * IDF)**\n\nI have applied this method in my model. And I have found the similarity in between with cosine distance.","metadata":{"_uuid":"29f885c7-5339-4d8d-b99d-0585784e6cc0","_cell_guid":"7d0903c4-9308-4059-9076-d544ee9f8224","trusted":true}},{"cell_type":"markdown","source":"","metadata":{"_uuid":"f3c53cfc-5bc3-4ff8-91df-f20953eac67a","_cell_guid":"2b2b995e-6d6c-4452-9948-0bbb90228ada","trusted":true}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# For Text\n\nimport matplotlib.pyplot as plt\nimport seaborn as sb\n\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.manifold import TSNE\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\n\n# Capture similarity \nfrom sklearn.metrics.pairwise import linear_kernel\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"fdaf36cf-4d9e-4ce2-9360-8417b6e445a1","_cell_guid":"c7427ab3-c6a6-433e-aa09-8469f15ff2fa","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-10-13T07:33:06.194729Z","iopub.execute_input":"2024-10-13T07:33:06.195208Z","iopub.status.idle":"2024-10-13T07:33:07.965888Z","shell.execute_reply.started":"2024-10-13T07:33:06.195163Z","shell.execute_reply":"2024-10-13T07:33:07.964662Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"/kaggle/input/musicaldata/musicaldata.csv\n","output_type":"stream"}]},{"cell_type":"markdown","source":"Let's get to know our dataset.","metadata":{"_uuid":"3c7eb90b-9f18-4613-ad7f-e6ce5f313c94","_cell_guid":"d039e612-5229-47f1-9b5c-0f3ced80c3f8","trusted":true}},{"cell_type":"code","source":"\n\ndata=pd.read_csv(\"/kaggle/input/musicaldata/musicaldata.csv\")\ndata.head(4000)","metadata":{"_uuid":"c0659919-7412-4f17-9e3c-62ea3bc44e4a","_cell_guid":"880bd5b0-e0cf-4df8-9da4-d40a3718e190","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-10-13T07:33:07.968352Z","iopub.execute_input":"2024-10-13T07:33:07.969507Z","iopub.status.idle":"2024-10-13T07:33:08.037031Z","shell.execute_reply.started":"2024-10-13T07:33:07.969427Z","shell.execute_reply":"2024-10-13T07:33:08.035890Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"      track id      genre   amazement   solemnity   tenderness   nostalgia  \\\n0            1  classical           0           1            0           0   \n1            1  classical           0           0            0           1   \n2            1  classical           0           0            0           1   \n3            1  classical           0           0            0           0   \n4            1  classical           0           0            0           1   \n...        ...        ...         ...         ...          ...         ...   \n3995       149       rock           0           0            1           0   \n3996       149       rock           0           0            0           1   \n3997       150       rock           0           0            0           0   \n3998       150       rock           0           1            0           0   \n3999       150       rock           0           1            0           1   \n\n       calmness   power   joyful_activation   tension   sadness   mood  \\\n0             0       0                   1         1         0      3   \n1             0       0                   0         0         0      3   \n2             0       0                   0         0         1      3   \n3             1       0                   0         0         0      3   \n4             1       0                   0         0         0      4   \n...         ...     ...                 ...       ...       ...    ...   \n3995          1       0                   0         0         1      4   \n3996          1       0                   0         0         1      3   \n3997          0       0                   0         1         0      3   \n3998          1       0                   0         0         0      3   \n3999          0       1                   0         0         0      2   \n\n       liked   disliked   age   gender  mother tongue  \n0          1          0    21        1        English  \n1          0          1    41        1          Dutch  \n2          0          0    24        1        English  \n3          0          0    32        0        Spanish  \n4          0          1    21        0        English  \n...      ...        ...   ...      ...            ...  \n3995       1          0    25        1          Dutch  \n3996       1          0    24        0         French  \n3997       0          1    38        0        English  \n3998       0          0    23        0          Dutch  \n3999       0          0    38        0          Dutch  \n\n[4000 rows x 17 columns]","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>track id</th>\n      <th>genre</th>\n      <th>amazement</th>\n      <th>solemnity</th>\n      <th>tenderness</th>\n      <th>nostalgia</th>\n      <th>calmness</th>\n      <th>power</th>\n      <th>joyful_activation</th>\n      <th>tension</th>\n      <th>sadness</th>\n      <th>mood</th>\n      <th>liked</th>\n      <th>disliked</th>\n      <th>age</th>\n      <th>gender</th>\n      <th>mother tongue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>classical</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>21</td>\n      <td>1</td>\n      <td>English</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>classical</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>41</td>\n      <td>1</td>\n      <td>Dutch</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>classical</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>24</td>\n      <td>1</td>\n      <td>English</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>classical</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>32</td>\n      <td>0</td>\n      <td>Spanish</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>classical</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>21</td>\n      <td>0</td>\n      <td>English</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3995</th>\n      <td>149</td>\n      <td>rock</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0</td>\n      <td>25</td>\n      <td>1</td>\n      <td>Dutch</td>\n    </tr>\n    <tr>\n      <th>3996</th>\n      <td>149</td>\n      <td>rock</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>24</td>\n      <td>0</td>\n      <td>French</td>\n    </tr>\n    <tr>\n      <th>3997</th>\n      <td>150</td>\n      <td>rock</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>38</td>\n      <td>0</td>\n      <td>English</td>\n    </tr>\n    <tr>\n      <th>3998</th>\n      <td>150</td>\n      <td>rock</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>23</td>\n      <td>0</td>\n      <td>Dutch</td>\n    </tr>\n    <tr>\n      <th>3999</th>\n      <td>150</td>\n      <td>rock</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>38</td>\n      <td>0</td>\n      <td>Dutch</td>\n    </tr>\n  </tbody>\n</table>\n<p>4000 rows × 17 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"> I wanted to make a suggestion depends on the cast members, description and \"listed_in\" which contains the shows type but there is another column named \"type\" it is a bit confusing I know. >","metadata":{"_uuid":"9c48687d-089d-4973-a961-1f0f3fd64222","_cell_guid":"bcbed148-1afc-4856-897c-2785218373a6","trusted":true}},{"cell_type":"markdown","source":"Drop nan values on these columns to make a proper matrix which contains linear_kernel values of selected strings.","metadata":{"_uuid":"ee78398d-7cba-4648-82c2-623599c65af1","_cell_guid":"e14fb5c6-5ecc-4d93-a689-051207177a1b","trusted":true}},{"cell_type":"code","source":"data.shape","metadata":{"_uuid":"1189e1aa-8de4-4b1d-9e73-9efc27b0a042","_cell_guid":"f9eb8186-2ed0-4582-9a22-b42262396c99","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-10-13T07:33:08.038298Z","iopub.execute_input":"2024-10-13T07:33:08.038675Z","iopub.status.idle":"2024-10-13T07:33:08.046585Z","shell.execute_reply.started":"2024-10-13T07:33:08.038636Z","shell.execute_reply":"2024-10-13T07:33:08.045399Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"(8407, 17)"},"metadata":{}}]},{"cell_type":"code","source":"data.info()","metadata":{"_uuid":"57deefcf-30eb-49c3-8d31-0ed102606343","_cell_guid":"f976d69f-6068-495e-8c26-877647ac4072","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-10-13T07:33:08.048210Z","iopub.execute_input":"2024-10-13T07:33:08.048680Z","iopub.status.idle":"2024-10-13T07:33:08.080505Z","shell.execute_reply.started":"2024-10-13T07:33:08.048626Z","shell.execute_reply":"2024-10-13T07:33:08.079336Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 8407 entries, 0 to 8406\nData columns (total 17 columns):\n #   Column              Non-Null Count  Dtype \n---  ------              --------------  ----- \n 0   track id            8407 non-null   int64 \n 1    genre              8407 non-null   object\n 2    amazement          8407 non-null   int64 \n 3    solemnity          8407 non-null   int64 \n 4    tenderness         8407 non-null   int64 \n 5    nostalgia          8407 non-null   int64 \n 6    calmness           8407 non-null   int64 \n 7    power              8407 non-null   int64 \n 8    joyful_activation  8407 non-null   int64 \n 9    tension            8407 non-null   int64 \n 10   sadness            8407 non-null   int64 \n 11   mood               8407 non-null   int64 \n 12   liked              8407 non-null   int64 \n 13   disliked           8407 non-null   int64 \n 14   age                8407 non-null   int64 \n 15   gender             8407 non-null   int64 \n 16   mother tongue      8407 non-null   object\ndtypes: int64(15), object(2)\nmemory usage: 1.1+ MB\n","output_type":"stream"}]},{"cell_type":"code","source":"data.isnull().sum()","metadata":{"_uuid":"8f43e982-b57f-44d4-8d27-1278f4d02e33","_cell_guid":"b58e0982-ab1a-46b4-92b5-5e160bcf3de5","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-10-13T07:33:08.083661Z","iopub.execute_input":"2024-10-13T07:33:08.084062Z","iopub.status.idle":"2024-10-13T07:33:08.095794Z","shell.execute_reply.started":"2024-10-13T07:33:08.084022Z","shell.execute_reply":"2024-10-13T07:33:08.094615Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"track id              0\n genre                0\n amazement            0\n solemnity            0\n tenderness           0\n nostalgia            0\n calmness             0\n power                0\n joyful_activation    0\n tension              0\n sadness              0\n mood                 0\n liked                0\n disliked             0\n age                  0\n gender               0\n mother tongue        0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"data.dropna(inplace = True)\ndata.isnull().sum().plot.bar()\nplt.show()","metadata":{"_uuid":"1323b0f8-6392-4630-9228-cdd495832606","_cell_guid":"43838d57-110b-4cc7-b9c7-af19f24439a3","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-10-13T07:33:08.097340Z","iopub.execute_input":"2024-10-13T07:33:08.097826Z","iopub.status.idle":"2024-10-13T07:33:08.476431Z","shell.execute_reply.started":"2024-10-13T07:33:08.097775Z","shell.execute_reply":"2024-10-13T07:33:08.475143Z"},"trusted":true},"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"data = data.drop(['track id'], axis = 1)\n\n\ndata","metadata":{"_uuid":"7544d9ad-5943-4c3e-b0a9-d182076eea2b","_cell_guid":"e548f824-acef-4dba-8309-c81ba42283ca","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-10-13T07:33:08.478236Z","iopub.execute_input":"2024-10-13T07:33:08.478837Z","iopub.status.idle":"2024-10-13T07:33:08.503828Z","shell.execute_reply.started":"2024-10-13T07:33:08.478782Z","shell.execute_reply":"2024-10-13T07:33:08.502655Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"          genre  amazement  solemnity  tenderness  nostalgia  calmness  power  \\\n0     classical          0          1           0          0         0      0   \n1     classical          0          0           0          1         0      0   \n2     classical          0          0           0          1         0      0   \n3     classical          0          0           0          0         1      0   \n4     classical          0          0           0          1         1      0   \n...         ...        ...        ...         ...        ...       ...    ...   \n8402        pop          1          1           0          0         0      0   \n8403        pop          0          0           0          1         0      0   \n8404        pop          0          0           0          0         0      0   \n8405        pop          1          0           0          0         0      0   \n8406        pop          1          0           0          0         0      0   \n\n      joyful_activation  tension  sadness  mood  liked  disliked  age  gender  \\\n0                     1        1        0     3      1         0   21       1   \n1                     0        0        0     3      0         1   41       1   \n2                     0        0        1     3      0         0   24       1   \n3                     0        0        0     3      0         0   32       0   \n4                     0        0        0     4      0         1   21       0   \n...                 ...      ...      ...   ...    ...       ...  ...     ...   \n8402                  1        0        0     3      0         0   26       1   \n8403                  1        0        1     3      0         1   29       0   \n8404                  0        1        0     4      0         1   34       1   \n8405                  1        1        0     5      0         0   39       1   \n8406                  0        1        0     4      0         1   18       1   \n\n     mother tongue  \n0          English  \n1            Dutch  \n2          English  \n3          Spanish  \n4          English  \n...            ...  \n8402       Russian  \n8403       Russian  \n8404        Polish  \n8405        French  \n8406       Russian  \n\n[8407 rows x 16 columns]","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>genre</th>\n      <th>amazement</th>\n      <th>solemnity</th>\n      <th>tenderness</th>\n      <th>nostalgia</th>\n      <th>calmness</th>\n      <th>power</th>\n      <th>joyful_activation</th>\n      <th>tension</th>\n      <th>sadness</th>\n      <th>mood</th>\n      <th>liked</th>\n      <th>disliked</th>\n      <th>age</th>\n      <th>gender</th>\n      <th>mother tongue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>classical</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>21</td>\n      <td>1</td>\n      <td>English</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>classical</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>41</td>\n      <td>1</td>\n      <td>Dutch</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>classical</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>24</td>\n      <td>1</td>\n      <td>English</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>classical</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>32</td>\n      <td>0</td>\n      <td>Spanish</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>classical</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>21</td>\n      <td>0</td>\n      <td>English</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>8402</th>\n      <td>pop</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>26</td>\n      <td>1</td>\n      <td>Russian</td>\n    </tr>\n    <tr>\n      <th>8403</th>\n      <td>pop</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>29</td>\n      <td>0</td>\n      <td>Russian</td>\n    </tr>\n    <tr>\n      <th>8404</th>\n      <td>pop</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>34</td>\n      <td>1</td>\n      <td>Polish</td>\n    </tr>\n    <tr>\n      <th>8405</th>\n      <td>pop</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>39</td>\n      <td>1</td>\n      <td>French</td>\n    </tr>\n    <tr>\n      <th>8406</th>\n      <td>pop</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>18</td>\n      <td>1</td>\n      <td>Russian</td>\n    </tr>\n  </tbody>\n</table>\n<p>8407 rows × 16 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{"_uuid":"1b44f336-d75d-4645-b483-44e5f887b560","_cell_guid":"b46575de-40e8-4968-b3a0-6e201c7a5169","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = data.sort_values(by=[' mood'], ascending=False)\ndata","metadata":{"_uuid":"124f2541-78e1-4fa5-8a5e-77d695687d0f","_cell_guid":"8bee36e8-a88f-4dc5-bfad-9d8d7d09b749","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-10-13T07:33:08.505368Z","iopub.execute_input":"2024-10-13T07:33:08.505850Z","iopub.status.idle":"2024-10-13T07:33:08.531549Z","shell.execute_reply.started":"2024-10-13T07:33:08.505808Z","shell.execute_reply":"2024-10-13T07:33:08.530527Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"           genre  amazement  solemnity  tenderness  nostalgia  calmness  \\\n5883  electronic          0          0           0          0         1   \n6562         pop          0          0           0          0         0   \n4458        rock          0          0           1          0         0   \n1178   classical          0          0           0          0         0   \n6543  electronic          0          0           0          0         0   \n...          ...        ...        ...         ...        ...       ...   \n5334  electronic          0          0           0          0         0   \n5332  electronic          0          0           1          0         1   \n2031   classical          0          0           0          1         1   \n2345   classical          0          0           0          1         0   \n1295   classical          0          0           1          0         1   \n\n      power  joyful_activation  tension  sadness  mood  liked  disliked  age  \\\n5883      0                  0        0        0     5      1         0   53   \n6562      0                  0        1        0     5      0         1   25   \n4458      0                  0        0        0     5      0         1   38   \n1178      0                  0        0        1     5      0         0   33   \n6543      1                  1        0        0     5      1         0   54   \n...     ...                ...      ...      ...   ...    ...       ...  ...   \n5334      0                  1        0        0     1      1         0   20   \n5332      0                  0        0        0     1      0         0   25   \n2031      0                  0        0        0     1      0         0   25   \n2345      0                  1        0        0     1      1         0   33   \n1295      0                  0        0        0     1      0         0   30   \n\n      gender mother tongue  \n5883       0       English  \n6562       1       English  \n4458       0         Dutch  \n1178       0       Russian  \n6543       0      Estonian  \n...      ...           ...  \n5334       1        French  \n5332       0       English  \n2031       1       Russian  \n2345       0       Russian  \n1295       1       English  \n\n[8407 rows x 16 columns]","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>genre</th>\n      <th>amazement</th>\n      <th>solemnity</th>\n      <th>tenderness</th>\n      <th>nostalgia</th>\n      <th>calmness</th>\n      <th>power</th>\n      <th>joyful_activation</th>\n      <th>tension</th>\n      <th>sadness</th>\n      <th>mood</th>\n      <th>liked</th>\n      <th>disliked</th>\n      <th>age</th>\n      <th>gender</th>\n      <th>mother tongue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5883</th>\n      <td>electronic</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0</td>\n      <td>53</td>\n      <td>0</td>\n      <td>English</td>\n    </tr>\n    <tr>\n      <th>6562</th>\n      <td>pop</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>1</td>\n      <td>25</td>\n      <td>1</td>\n      <td>English</td>\n    </tr>\n    <tr>\n      <th>4458</th>\n      <td>rock</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>1</td>\n      <td>38</td>\n      <td>0</td>\n      <td>Dutch</td>\n    </tr>\n    <tr>\n      <th>1178</th>\n      <td>classical</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>33</td>\n      <td>0</td>\n      <td>Russian</td>\n    </tr>\n    <tr>\n      <th>6543</th>\n      <td>electronic</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0</td>\n      <td>54</td>\n      <td>0</td>\n      <td>Estonian</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>5334</th>\n      <td>electronic</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>20</td>\n      <td>1</td>\n      <td>French</td>\n    </tr>\n    <tr>\n      <th>5332</th>\n      <td>electronic</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>25</td>\n      <td>0</td>\n      <td>English</td>\n    </tr>\n    <tr>\n      <th>2031</th>\n      <td>classical</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>25</td>\n      <td>1</td>\n      <td>Russian</td>\n    </tr>\n    <tr>\n      <th>2345</th>\n      <td>classical</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>33</td>\n      <td>0</td>\n      <td>Russian</td>\n    </tr>\n    <tr>\n      <th>1295</th>\n      <td>classical</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>30</td>\n      <td>1</td>\n      <td>English</td>\n    </tr>\n  </tbody>\n</table>\n<p>8407 rows × 16 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"%%capture\nsong_vectorizer = CountVectorizer(lowercase=False)\nsong_vectorizer.fit(data[' genre'])","metadata":{"_uuid":"7bbefcab-7638-47dd-b592-8e5571e572fd","_cell_guid":"54454efe-e88b-45a0-9d25-f80c5a0035ba","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-10-13T07:33:08.533041Z","iopub.execute_input":"2024-10-13T07:33:08.533504Z","iopub.status.idle":"2024-10-13T07:33:08.594800Z","shell.execute_reply.started":"2024-10-13T07:33:08.533433Z","shell.execute_reply":"2024-10-13T07:33:08.593722Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"","metadata":{"_uuid":"92fb6efd-e669-4ec6-b8d5-9159816d3ccd","_cell_guid":"8bf19c95-4be4-441f-b10f-1e5753deeace","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = data.sort_values(by=[' mood'], ascending=False).head(8407)","metadata":{"_uuid":"37cb1e22-e2f2-4a5c-a1ab-2e1cbe3312c8","_cell_guid":"f8287be2-15db-4368-9cdd-a9380689fd06","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-10-13T07:33:08.596500Z","iopub.execute_input":"2024-10-13T07:33:08.596889Z","iopub.status.idle":"2024-10-13T07:33:08.603445Z","shell.execute_reply.started":"2024-10-13T07:33:08.596850Z","shell.execute_reply":"2024-10-13T07:33:08.602557Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"def get_similarities(song_genre, datas):\n\n# Getting vector for the input song.\n    text_array1 = song_vectorizer.transform(datas[datas[' genre']==song_genre][' mother tongue']).toarray()\n    num_array1 = datas[datas[' genre']==song_genre].select_dtypes(include=np.number).to_numpy()\n\n# We will store similarity for each row of the dataset.\n    sim = []\n    for idx, row in data.iterrows():\n\t    genre = row[' genre']\n\t\n\t# Getting vector for current song.\n\t    text_array2 = song_vectorizer.transform(datas[datas[' genre']==genre][' mother tongue']).toarray()\n\t    num_array2 = datas[datas[' genre']==genre].select_dtypes(include=np.number).to_numpy()\n\n\t# Calculating similarities for text as well as numeric features\n\t    text_sim = cosine_similarity(text_array1, text_array2)[0][0]\n\t    num_sim = cosine_similarity(num_array1, num_array2)[0][0]\n\t    sim.append(text_sim + num_sim)\n\t\n    return sim","metadata":{"_uuid":"2446d4e8-3d65-4c9f-9814-5117a85fbc4c","_cell_guid":"6ce791c5-fd3a-439c-b747-6861fc32c5c6","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-10-13T07:33:08.604749Z","iopub.execute_input":"2024-10-13T07:33:08.605122Z","iopub.status.idle":"2024-10-13T07:33:08.616061Z","shell.execute_reply.started":"2024-10-13T07:33:08.605065Z","shell.execute_reply":"2024-10-13T07:33:08.615025Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"def recommend_songs(song_genre, datas=data):\n  # Base case\n   if data[data[' genre'] == song_genre].shape[0] == 0:\n        print('This song is not so popular')\n     \n        for song in datas.sample(n=5)[' genre'].values:\n            print(song)\n        return\n   \n   datas['similarity_factor'] = get_similarities(song_genre, datas)\n \n   datas.sort_values(by=['similarity_factor', ' mood'],\n                   ascending = [False, False],\n                   inplace=True)\n   \n  # First song will be the input song itself as the similarity will be highest.\n   display(datas[[' genre', ' age', ' mother tongue']][2:7])","metadata":{"_uuid":"f9b1672e-b8d3-41f2-bda3-d18fc6306d93","_cell_guid":"18501619-640c-418d-bde4-a98dfe9325f9","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-10-13T07:33:08.617517Z","iopub.execute_input":"2024-10-13T07:33:08.617957Z","iopub.status.idle":"2024-10-13T07:33:08.628758Z","shell.execute_reply.started":"2024-10-13T07:33:08.617908Z","shell.execute_reply":"2024-10-13T07:33:08.627732Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"recommend_songs('classical')","metadata":{"_uuid":"444cba3e-4bcc-4e0c-9f8d-920a04e37178","_cell_guid":"ce074402-e969-4fe1-85bd-c5908c6bff61","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-10-13T07:33:08.630468Z","iopub.execute_input":"2024-10-13T07:33:08.631030Z","iopub.status.idle":"2024-10-13T07:41:43.863235Z","shell.execute_reply.started":"2024-10-13T07:33:08.630969Z","shell.execute_reply":"2024-10-13T07:41:43.862031Z"},"trusted":true},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"         genre  age mother tongue\n246  classical   45        Korean\n528  classical   46       English\n85   classical   34    Portuguese\n580  classical   46       English\n587  classical   55         Dutch","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>genre</th>\n      <th>age</th>\n      <th>mother tongue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>246</th>\n      <td>classical</td>\n      <td>45</td>\n      <td>Korean</td>\n    </tr>\n    <tr>\n      <th>528</th>\n      <td>classical</td>\n      <td>46</td>\n      <td>English</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>classical</td>\n      <td>34</td>\n      <td>Portuguese</td>\n    </tr>\n    <tr>\n      <th>580</th>\n      <td>classical</td>\n      <td>46</td>\n      <td>English</td>\n    </tr>\n    <tr>\n      <th>587</th>\n      <td>classical</td>\n      <td>55</td>\n      <td>Dutch</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"\nimport streamlit as st\nst.header(\"music genre based age Recommendation System\" )","metadata":{"_uuid":"be98c715-a3eb-413d-9a68-b758d11db6f1","_cell_guid":"dc6a052e-9e86-4803-b4c3-1b3cc2feb275","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-10-13T08:01:06.254773Z","iopub.execute_input":"2024-10-13T08:01:06.256183Z","iopub.status.idle":"2024-10-13T08:02:24.385650Z","shell.execute_reply.started":"2024-10-13T08:01:06.256130Z","shell.execute_reply":"2024-10-13T08:02:24.383554Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"conda 24.9.0\n\u001b[33mWARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x7981a66497b0>: Failed to establish a new connection: [Errno -3] Temporary failure in name resolution')': /simple/streamlit/\u001b[0m\u001b[33m\n\u001b[0m\u001b[33mWARNING: Retrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x7981a6649ab0>: Failed to establish a new connection: [Errno -3] Temporary failure in name resolution')': /simple/streamlit/\u001b[0m\u001b[33m\n\u001b[0m\u001b[33mWARNING: Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x7981a6649d50>: Failed to establish a new connection: [Errno -3] Temporary failure in name resolution')': /simple/streamlit/\u001b[0m\u001b[33m\n\u001b[0m^C\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)","Cell \u001b[0;32mIn[21], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39msystem(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mconda -V\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      2\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39msystem(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpip install -q streamlit\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mstreamlit\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mst\u001b[39;00m\n\u001b[1;32m      5\u001b[0m st\u001b[38;5;241m.\u001b[39mheader(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmusic genre based age Recommendation System\u001b[39m\u001b[38;5;124m\"\u001b[39m )\n","\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'streamlit'"],"ename":"ModuleNotFoundError","evalue":"No module named 'streamlit'","output_type":"error"}]},{"cell_type":"code","source":"genre = data['genre'].values\ngenre","metadata":{"_uuid":"1b266fc4-5690-46d4-89d9-e7bee55b51ce","_cell_guid":"28f4c7e0-a5ad-4619-a36e-fa7e80c2ea72","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-10-13T07:41:44.405653Z","iopub.status.idle":"2024-10-13T07:41:44.406272Z","shell.execute_reply.started":"2024-10-13T07:41:44.405930Z","shell.execute_reply":"2024-10-13T07:41:44.405959Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if st.button('if st.button('Show Recommendation'):\n      recommended_age = recommend_songs(song_genre, datas=data)\n      recommended_age","metadata":{"_uuid":"b16edf29-3b36-4b08-8edf-0bbf284c9b29","_cell_guid":"9264a102-2179-40ec-8765-d8359ed601f1","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-10-13T07:41:44.408245Z","iopub.status.idle":"2024-10-13T07:41:44.408817Z","shell.execute_reply.started":"2024-10-13T07:41:44.408520Z","shell.execute_reply":"2024-10-13T07:41:44.408550Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"_uuid":"e1161ad4-d84b-413c-87b8-35e9f91686a5","_cell_guid":"7808e3c8-4331-43d6-a974-a6946b44cadd","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"_uuid":"f66e3d74-1fd8-4268-a34a-4dda1596f4c7","_cell_guid":"72985dcb-49dd-42ba-bc4b-c4d722190ba8","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"In this above. My model brought the recommendations I wanted to bring acording to the mood of the songs of different genres . Firstly it brought to Transformers movies.\n\n# Conclusion\n\n* In this notebook. I worked to explain content based recommendation system.\n* Content based RS models are powerful in new item recommendation. \n* In general it is used TF-IDF and Word2Vec model while content based RS designs.\n* TF-IDF method is the method which explains words frequency in document.\n* It can be created a recommendation model by using cosine distance with the aid of TF-IDF weights.","metadata":{"_uuid":"44c83c1a-3c0f-453d-b4ff-7fc5680f4c4a","_cell_guid":"03c84478-9f90-49ea-8c39-80e5b748160d","trusted":true}}]}
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