hit-songs-prediction-and-eda.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/sridharstreaks/hit-songs-prediction-and-eda?scriptVersionId=121543169\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown","outputs":[],"execution_count":0},{"cell_type":"markdown","id":"17438b29","metadata":{"papermill":{"duration":0.010016,"end_time":"2023-03-09T09:43:50.072538","exception":false,"start_time":"2023-03-09T09:43:50.062522","status":"completed"},"tags":[]},"source":["# Predicting Songs that'll be hit using various ML models\n","\n","#### This notebook provides EDA and Prediction models on the most streamed songs on spotify.\n","\n","**I did some simple machine learning model that tries to predict \"streams\" using various parameters. This dataset is simple and limited thus it was quite easy to build models upon. I also considered all available variables on the dataset which i felt would be important. one can change these metrics to achieve better results. I also did EDA through Plotly plots on the way to make ML models. I used plotly as it gives the user to tinker around with the graph using selection and also it provides hover data which makes it very helpful. I'll hide the plot code to make it feel like a report.**\n","\n","#### Let's Begin"]},{"cell_type":"markdown","id":"5484b6e4","metadata":{"papermill":{"duration":0.00969,"end_time":"2023-03-09T09:43:50.091981","exception":false,"start_time":"2023-03-09T09:43:50.082291","status":"completed"},"tags":[]},"source":["--------------------------------------------------------------------------------------------------------"]},{"cell_type":"markdown","id":"acfdfefc","metadata":{"papermill":{"duration":0.008748,"end_time":"2023-03-09T09:43:50.111324","exception":false,"start_time":"2023-03-09T09:43:50.102576","status":"completed"},"tags":[]},"source":["# Loading necessary Libraries and Data"]},{"cell_type":"code","execution_count":1,"id":"496006ab","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:43:50.133367Z","iopub.status.busy":"2023-03-09T09:43:50.132794Z","iopub.status.idle":"2023-03-09T09:43:52.543865Z","shell.execute_reply":"2023-03-09T09:43:52.542822Z"},"papermill":{"duration":2.428328,"end_time":"2023-03-09T09:43:52.55014","exception":false,"start_time":"2023-03-09T09:43:50.121812","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["        <script type=\"text/javascript\">\n","        window.PlotlyConfig = {MathJaxConfig: 'local'};\n","        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n","        if (typeof require !== 'undefined') {\n","        require.undef(\"plotly\");\n","        requirejs.config({\n","            paths: {\n","                'plotly': ['https://cdn.plot.ly/plotly-2.18.0.min']\n","            }\n","        });\n","        require(['plotly'], function(Plotly) {\n","            window._Plotly = Plotly;\n","        });\n","        }\n","        </script>\n","        "]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Libraries Loaded\n"]}],"source":["import plotly.express as px\n","import pandas as pd\n","\n","from plotly.offline import init_notebook_mode, iplot\n","init_notebook_mode(connected=True)\n","\n","print(\"Libraries Loaded\")"]},{"cell_type":"code","execution_count":2,"id":"c03dbb3d","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:43:52.572293Z","iopub.status.busy":"2023-03-09T09:43:52.571803Z","iopub.status.idle":"2023-03-09T09:43:52.606716Z","shell.execute_reply":"2023-03-09T09:43:52.604406Z"},"papermill":{"duration":0.049157,"end_time":"2023-03-09T09:43:52.610214","exception":false,"start_time":"2023-03-09T09:43:52.561057","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Imported datasets\n"]}],"source":["# Load the datasets\n","features_df = pd.read_csv(\"/kaggle/input/most-streamed-songs-all-time/Features.csv\")\n","streams_df = pd.read_csv(\"/kaggle/input/most-streamed-songs-all-time/Streams.csv\")\n","\n","print(\"Imported datasets\")"]},{"cell_type":"markdown","id":"f43e6284","metadata":{"papermill":{"duration":0.010604,"end_time":"2023-03-09T09:43:52.631514","exception":false,"start_time":"2023-03-09T09:43:52.62091","status":"completed"},"tags":[]},"source":["--------------------------------------------------------------------------------------------------------"]},{"cell_type":"markdown","id":"010eaee8","metadata":{"papermill":{"duration":0.008922,"end_time":"2023-03-09T09:43:52.649905","exception":false,"start_time":"2023-03-09T09:43:52.640983","status":"completed"},"tags":[]},"source":["# Overviewing the Datas"]},{"cell_type":"code","execution_count":3,"id":"15c87969","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:43:52.672922Z","iopub.status.busy":"2023-03-09T09:43:52.672495Z","iopub.status.idle":"2023-03-09T09:43:52.710597Z","shell.execute_reply":"2023-03-09T09:43:52.708727Z"},"papermill":{"duration":0.052689,"end_time":"2023-03-09T09:43:52.713532","exception":false,"start_time":"2023-03-09T09:43:52.660843","status":"completed"},"tags":[]},"outputs":[{"data":{"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>id</th>\n","      <th>name</th>\n","      <th>duration</th>\n","      <th>energy</th>\n","      <th>key</th>\n","      <th>loudness</th>\n","      <th>mode</th>\n","      <th>speechiness</th>\n","      <th>acousticness</th>\n","      <th>instrumentalness</th>\n","      <th>liveness</th>\n","      <th>valence</th>\n","      <th>tempo</th>\n","      <th>danceability</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0VjIjW4GlUZAMYd2vXMi3b</td>\n","      <td>Blinding Lights</td>\n","      <td>3.33</td>\n","      <td>0.730</td>\n","      <td>1</td>\n","      <td>-5.934</td>\n","      <td>1</td>\n","      <td>0.0598</td>\n","      <td>0.00146</td>\n","      <td>0.000095</td>\n","      <td>0.0897</td>\n","      <td>0.334</td>\n","      <td>171.005</td>\n","      <td>0.514</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>7qiZfU4dY1lWllzX7mPBI3</td>\n","      <td>Shape of You</td>\n","      <td>3.90</td>\n","      <td>0.652</td>\n","      <td>1</td>\n","      <td>-3.183</td>\n","      <td>0</td>\n","      <td>0.0802</td>\n","      <td>0.58100</td>\n","      <td>0.000000</td>\n","      <td>0.0931</td>\n","      <td>0.931</td>\n","      <td>95.977</td>\n","      <td>0.825</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2XU0oxnq2qxCpomAAuJY8K</td>\n","      <td>Dance Monkey</td>\n","      <td>3.49</td>\n","      <td>0.588</td>\n","      <td>6</td>\n","      <td>-6.400</td>\n","      <td>0</td>\n","      <td>0.0924</td>\n","      <td>0.69200</td>\n","      <td>0.000104</td>\n","      <td>0.1490</td>\n","      <td>0.513</td>\n","      <td>98.027</td>\n","      <td>0.824</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>7qEHsqek33rTcFNT9PFqLf</td>\n","      <td>Someone You Loved</td>\n","      <td>3.04</td>\n","      <td>0.405</td>\n","      <td>1</td>\n","      <td>-5.679</td>\n","      <td>1</td>\n","      <td>0.0319</td>\n","      <td>0.75100</td>\n","      <td>0.000000</td>\n","      <td>0.1050</td>\n","      <td>0.446</td>\n","      <td>109.891</td>\n","      <td>0.501</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0e7ipj03S05BNilyu5bRzt</td>\n","      <td>Rockstar</td>\n","      <td>3.64</td>\n","      <td>0.520</td>\n","      <td>5</td>\n","      <td>-6.136</td>\n","      <td>0</td>\n","      <td>0.0712</td>\n","      <td>0.12400</td>\n","      <td>0.000070</td>\n","      <td>0.1310</td>\n","      <td>0.129</td>\n","      <td>159.801</td>\n","      <td>0.585</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                       id               name  duration  energy  key  loudness  \\\n","0  0VjIjW4GlUZAMYd2vXMi3b    Blinding Lights      3.33   0.730    1    -5.934   \n","1  7qiZfU4dY1lWllzX7mPBI3       Shape of You      3.90   0.652    1    -3.183   \n","2  2XU0oxnq2qxCpomAAuJY8K       Dance Monkey      3.49   0.588    6    -6.400   \n","3  7qEHsqek33rTcFNT9PFqLf  Someone You Loved      3.04   0.405    1    -5.679   \n","4  0e7ipj03S05BNilyu5bRzt           Rockstar      3.64   0.520    5    -6.136   \n","\n","   mode  speechiness  acousticness  instrumentalness  liveness  valence  \\\n","0     1       0.0598       0.00146          0.000095    0.0897    0.334   \n","1     0       0.0802       0.58100          0.000000    0.0931    0.931   \n","2     0       0.0924       0.69200          0.000104    0.1490    0.513   \n","3     1       0.0319       0.75100          0.000000    0.1050    0.446   \n","4     0       0.0712       0.12400          0.000070    0.1310    0.129   \n","\n","     tempo  danceability  \n","0  171.005         0.514  \n","1   95.977         0.825  \n","2   98.027         0.824  \n","3  109.891         0.501  \n","4  159.801         0.585  "]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["features_df.head()"]},{"cell_type":"code","execution_count":4,"id":"2d4041c2","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:43:52.736578Z","iopub.status.busy":"2023-03-09T09:43:52.736154Z","iopub.status.idle":"2023-03-09T09:43:52.750296Z","shell.execute_reply":"2023-03-09T09:43:52.748513Z"},"papermill":{"duration":0.030061,"end_time":"2023-03-09T09:43:52.753635","exception":false,"start_time":"2023-03-09T09:43:52.723574","status":"completed"},"tags":[]},"outputs":[{"data":{"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>Song</th>\n","      <th>Artist</th>\n","      <th>Streams (Billions)</th>\n","      <th>Release Date</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Blinding Lights</td>\n","      <td>The Weeknd</td>\n","      <td>3.449</td>\n","      <td>29-Nov-19</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Shape of You</td>\n","      <td>Ed Sheeran</td>\n","      <td>3.398</td>\n","      <td>06-Jan-17</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Dance Monkey</td>\n","      <td>Tones And I</td>\n","      <td>2.770</td>\n","      <td>10-May-19</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Someone You Loved</td>\n","      <td>Lewis Capaldi</td>\n","      <td>2.680</td>\n","      <td>08-Nov-18</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Rockstar</td>\n","      <td>Post Malone featuring 21 Savage</td>\n","      <td>2.620</td>\n","      <td>15-Sep-17</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                Song                           Artist  Streams (Billions)  \\\n","0    Blinding Lights                       The Weeknd               3.449   \n","1       Shape of You                       Ed Sheeran               3.398   \n","2       Dance Monkey                      Tones And I               2.770   \n","3  Someone You Loved                    Lewis Capaldi               2.680   \n","4           Rockstar  Post Malone featuring 21 Savage               2.620   \n","\n","  Release Date  \n","0    29-Nov-19  \n","1    06-Jan-17  \n","2    10-May-19  \n","3    08-Nov-18  \n","4    15-Sep-17  "]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["streams_df.head()"]},{"cell_type":"code","execution_count":5,"id":"4eb199dd","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:43:52.777776Z","iopub.status.busy":"2023-03-09T09:43:52.776144Z","iopub.status.idle":"2023-03-09T09:43:52.789177Z","shell.execute_reply":"2023-03-09T09:43:52.788184Z"},"papermill":{"duration":0.02748,"end_time":"2023-03-09T09:43:52.791863","exception":false,"start_time":"2023-03-09T09:43:52.764383","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["Song                  0\n","Artist                0\n","Streams (Billions)    0\n","Release Date          0\n","dtype: int64"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["streams_df.isna().sum()"]},{"cell_type":"code","execution_count":6,"id":"132ed8bc","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:43:52.816813Z","iopub.status.busy":"2023-03-09T09:43:52.815907Z","iopub.status.idle":"2023-03-09T09:43:52.827568Z","shell.execute_reply":"2023-03-09T09:43:52.825739Z"},"papermill":{"duration":0.027235,"end_time":"2023-03-09T09:43:52.83059","exception":false,"start_time":"2023-03-09T09:43:52.803355","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["id                  0\n","name                0\n","duration            0\n","energy              0\n","key                 0\n","loudness            0\n","mode                0\n","speechiness         0\n","acousticness        0\n","instrumentalness    0\n","liveness            0\n","valence             0\n","tempo               0\n","danceability        0\n","dtype: int64"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["features_df.isna().sum()"]},{"cell_type":"markdown","id":"57eb9b9b","metadata":{"papermill":{"duration":0.00973,"end_time":"2023-03-09T09:43:52.850482","exception":false,"start_time":"2023-03-09T09:43:52.840752","status":"completed"},"tags":[]},"source":["--------------------------------------------------------------------------------------------------------"]},{"cell_type":"markdown","id":"1bf5c9d4","metadata":{"papermill":{"duration":0.009501,"end_time":"2023-03-09T09:43:52.870011","exception":false,"start_time":"2023-03-09T09:43:52.86051","status":"completed"},"tags":[]},"source":["# EDA through Plots"]},{"cell_type":"code","execution_count":7,"id":"c7ec9cf6","metadata":{"_kg_hide-input":true,"execution":{"iopub.execute_input":"2023-03-09T09:43:52.891786Z","iopub.status.busy":"2023-03-09T09:43:52.891356Z","iopub.status.idle":"2023-03-09T09:43:54.853884Z","shell.execute_reply":"2023-03-09T09:43:54.85228Z"},"papermill":{"duration":1.978669,"end_time":"2023-03-09T09:43:54.858608","exception":false,"start_time":"2023-03-09T09:43:52.879939","status":"completed"},"tags":[]},"outputs":[{"data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"customdata":[["Blinding Lights",3.33],["Shape of You",3.9],["Dance Monkey",3.49],["Someone You Loved",3.04],["Rockstar",3.64],["Sunflower",2.63],["One Dance",2.9],["Closer",4.08],["Stay",4.01],["Believer",3.41],["Señorita",3.18],["Perfect",2.96],["Heat Waves",3.98],["Say You Won't Let Go",3.52],["Starboy",3.84],["Bad Guy",3.23],["Thinking Out Loud",4.69],["Lucid Dreams",4.0],["Don't Start 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Perceptron)"]},{"cell_type":"markdown","id":"42ce72fa","metadata":{"papermill":{"duration":0.102434,"end_time":"2023-03-09T09:44:00.700763","exception":false,"start_time":"2023-03-09T09:44:00.598329","status":"completed"},"tags":[]},"source":["## Data preprocessing"]},{"cell_type":"code","execution_count":19,"id":"581a1935","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:44:00.913048Z","iopub.status.busy":"2023-03-09T09:44:00.912627Z","iopub.status.idle":"2023-03-09T09:44:01.199522Z","shell.execute_reply":"2023-03-09T09:44:01.198056Z"},"papermill":{"duration":0.396426,"end_time":"2023-03-09T09:44:01.203332","exception":false,"start_time":"2023-03-09T09:44:00.806906","status":"completed"},"tags":[]},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","from sklearn.linear_model import LinearRegression\n","from sklearn.metrics import mean_squared_error\n","\n","# Define the input and target variables\n","X = df.drop([\"id\",\"mode\",\"name\", \"Artist\", \"Streams (Billions)\", \"Release Date\"], axis=1)\n","y = df[\"Streams (Billions)\"]"]},{"cell_type":"code","execution_count":20,"id":"525a5d95","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:44:01.404555Z","iopub.status.busy":"2023-03-09T09:44:01.404064Z","iopub.status.idle":"2023-03-09T09:44:01.413104Z","shell.execute_reply":"2023-03-09T09:44:01.4116Z"},"papermill":{"duration":0.113109,"end_time":"2023-03-09T09:44:01.416185","exception":false,"start_time":"2023-03-09T09:44:01.303076","status":"completed"},"tags":[]},"outputs":[],"source":["# Split the data into training and test sets\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"]},{"cell_type":"markdown","id":"3bed7695","metadata":{"papermill":{"duration":0.106765,"end_time":"2023-03-09T09:44:01.624845","exception":false,"start_time":"2023-03-09T09:44:01.51808","status":"completed"},"tags":[]},"source":["--------------------------------------------------------------------------------------------------------"]},{"cell_type":"markdown","id":"9a05dd0d","metadata":{"papermill":{"duration":0.100444,"end_time":"2023-03-09T09:44:01.833356","exception":false,"start_time":"2023-03-09T09:44:01.732912","status":"completed"},"tags":[]},"source":["## Linear Regression"]},{"cell_type":"code","execution_count":21,"id":"fb561679","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:44:02.110622Z","iopub.status.busy":"2023-03-09T09:44:02.110187Z","iopub.status.idle":"2023-03-09T09:44:02.125746Z","shell.execute_reply":"2023-03-09T09:44:02.124208Z"},"papermill":{"duration":0.191439,"end_time":"2023-03-09T09:44:02.128484","exception":false,"start_time":"2023-03-09T09:44:01.937045","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Mean Squared Error: 0.4081881758515456\n"]}],"source":["# Train a linear regression model\n","model = LinearRegression()\n","model.fit(X_train, y_train)\n","\n","# Make predictions on the test set\n","y_pred = model.predict(X_test)\n","\n","# Calculate the mean squared error\n","mse = mean_squared_error(y_test, y_pred)\n","print(\"Mean Squared Error:\", mse)"]},{"cell_type":"markdown","id":"7db1e9f7","metadata":{"papermill":{"duration":0.105065,"end_time":"2023-03-09T09:44:02.338786","exception":false,"start_time":"2023-03-09T09:44:02.233721","status":"completed"},"tags":[]},"source":["## Random Forest Regressor"]},{"cell_type":"code","execution_count":22,"id":"77f91e66","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:44:02.552849Z","iopub.status.busy":"2023-03-09T09:44:02.552434Z","iopub.status.idle":"2023-03-09T09:44:03.041044Z","shell.execute_reply":"2023-03-09T09:44:03.039542Z"},"papermill":{"duration":0.598337,"end_time":"2023-03-09T09:44:03.044319","exception":false,"start_time":"2023-03-09T09:44:02.445982","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Mean Squared Error: 0.22715415784557766\n"]}],"source":["# Import the required libraries\n","from sklearn.ensemble import RandomForestRegressor\n","\n","# Instantiate the model\n","rf = RandomForestRegressor(n_estimators=100, random_state=42)\n","\n","# Train the model\n","rf.fit(X_train, y_train)\n","\n","# Make predictions on the test set\n","y_pred = rf.predict(X_test)\n","\n","# Calculate the mean squared error\n","mse = mean_squared_error(y_test, y_pred)\n","\n","print(\"Mean Squared Error:\", mse)"]},{"cell_type":"markdown","id":"5a8c577e","metadata":{"papermill":{"duration":0.107537,"end_time":"2023-03-09T09:44:03.255338","exception":false,"start_time":"2023-03-09T09:44:03.147801","status":"completed"},"tags":[]},"source":["## XGBoost Regressor"]},{"cell_type":"code","execution_count":23,"id":"5bd3a18e","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:44:03.470073Z","iopub.status.busy":"2023-03-09T09:44:03.469199Z","iopub.status.idle":"2023-03-09T09:44:06.08634Z","shell.execute_reply":"2023-03-09T09:44:06.084429Z"},"papermill":{"duration":2.728812,"end_time":"2023-03-09T09:44:06.089365","exception":false,"start_time":"2023-03-09T09:44:03.360553","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Mean Squared Error (XGBoost Regressor): 0.239116\n"]}],"source":["import xgboost as xgb\n","\n","# Train an XGBoost regressor model\n","model_xgb = xgb.XGBRegressor(objective='reg:squarederror', n_estimators=1000, learning_rate=0.05)\n","model_xgb.fit(X_train, y_train)\n","\n","# Make predictions on the test set\n","y_pred_xgb = model_xgb.predict(X_test)\n","\n","# Evaluate the performance of the XGBoost regressor model using mean squared error\n","mse_xgb = mean_squared_error(y_test, y_pred_xgb)\n","print(\"Mean Squared Error (XGBoost Regressor): {:.6f}\".format(mse_xgb))"]},{"cell_type":"markdown","id":"d119a843","metadata":{"papermill":{"duration":0.101174,"end_time":"2023-03-09T09:44:06.291212","exception":false,"start_time":"2023-03-09T09:44:06.190038","status":"completed"},"tags":[]},"source":["## Support Vector Regressor"]},{"cell_type":"code","execution_count":24,"id":"62ca34f1","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:44:06.496066Z","iopub.status.busy":"2023-03-09T09:44:06.49565Z","iopub.status.idle":"2023-03-09T09:44:06.509688Z","shell.execute_reply":"2023-03-09T09:44:06.508011Z"},"papermill":{"duration":0.120363,"end_time":"2023-03-09T09:44:06.512866","exception":false,"start_time":"2023-03-09T09:44:06.392503","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Mean Squared Error: 0.23321553058983402\n"]}],"source":["from sklearn.svm import SVR\n","\n","# Train a support vector regressor model\n","model = SVR(kernel='rbf')\n","model.fit(X_train, y_train)\n","\n","# Make predictions on the testing set\n","y_pred = model.predict(X_test)\n","\n","# Evaluate the model using mean squared error\n","mse = mean_squared_error(y_test, y_pred)\n","print(\"Mean Squared Error:\", mse)"]},{"cell_type":"markdown","id":"ae8b4a56","metadata":{"papermill":{"duration":0.102599,"end_time":"2023-03-09T09:44:06.718984","exception":false,"start_time":"2023-03-09T09:44:06.616385","status":"completed"},"tags":[]},"source":["## Neural Networks"]},{"cell_type":"code","execution_count":25,"id":"4e8395ec","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:44:06.993109Z","iopub.status.busy":"2023-03-09T09:44:06.992642Z","iopub.status.idle":"2023-03-09T09:44:23.616885Z","shell.execute_reply":"2023-03-09T09:44:23.614943Z"},"papermill":{"duration":16.731447,"end_time":"2023-03-09T09:44:23.620483","exception":false,"start_time":"2023-03-09T09:44:06.889036","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/100\n","2/2 [==============================] - 1s 265ms/step - loss: 180.4926 - mse: 180.4926 - val_loss: 49.8875 - val_mse: 49.8875\n","Epoch 2/100\n","2/2 [==============================] - 0s 35ms/step - loss: 34.5801 - mse: 34.5801 - val_loss: 0.6199 - val_mse: 0.6199\n","Epoch 3/100\n","2/2 [==============================] - 0s 37ms/step - loss: 4.7016 - mse: 4.7016 - val_loss: 27.3488 - val_mse: 27.3488\n","Epoch 4/100\n","2/2 [==============================] - 0s 37ms/step - loss: 34.3306 - mse: 34.3306 - val_loss: 49.3568 - val_mse: 49.3568\n","Epoch 5/100\n","2/2 [==============================] - 0s 37ms/step - loss: 47.9452 - mse: 47.9452 - val_loss: 38.8373 - val_mse: 38.8373\n","Epoch 6/100\n","2/2 [==============================] - 0s 39ms/step - loss: 32.3073 - mse: 32.3073 - val_loss: 15.7965 - val_mse: 15.7965\n","Epoch 7/100\n","2/2 [==============================] - 0s 60ms/step - loss: 10.6995 - mse: 10.6995 - val_loss: 1.7131 - val_mse: 1.7131\n","Epoch 8/100\n","2/2 [==============================] - 0s 40ms/step - loss: 1.0107 - mse: 1.0107 - val_loss: 2.7650 - val_mse: 2.7650\n","Epoch 9/100\n","2/2 [==============================] - 0s 38ms/step - loss: 4.7069 - mse: 4.7069 - val_loss: 10.1402 - val_mse: 10.1402\n","Epoch 10/100\n","2/2 [==============================] - 0s 35ms/step - loss: 12.3413 - mse: 12.3413 - val_loss: 13.1724 - val_mse: 13.1724\n","Epoch 11/100\n","2/2 [==============================] - 0s 40ms/step - loss: 13.5301 - mse: 13.5301 - val_loss: 9.3149 - val_mse: 9.3149\n","Epoch 12/100\n","2/2 [==============================] - 0s 38ms/step - loss: 8.4900 - mse: 8.4900 - val_loss: 3.0211 - val_mse: 3.0211\n","Epoch 13/100\n","2/2 [==============================] - 0s 38ms/step - loss: 2.0002 - mse: 2.0002 - val_loss: 0.5717 - val_mse: 0.5717\n","Epoch 14/100\n","2/2 [==============================] - 0s 37ms/step - loss: 0.5350 - mse: 0.5350 - val_loss: 2.3683 - val_mse: 2.3683\n","Epoch 15/100\n","2/2 [==============================] - 0s 35ms/step - loss: 2.5194 - mse: 2.5194 - val_loss: 4.9424 - val_mse: 4.9424\n","Epoch 16/100\n","2/2 [==============================] - 0s 36ms/step - loss: 4.5071 - mse: 4.5071 - val_loss: 5.1177 - val_mse: 5.1177\n","Epoch 17/100\n","2/2 [==============================] - 0s 38ms/step - loss: 4.0441 - mse: 4.0441 - val_loss: 3.0502 - val_mse: 3.0502\n","Epoch 18/100\n","2/2 [==============================] - 0s 38ms/step - loss: 1.9503 - mse: 1.9503 - val_loss: 1.0180 - val_mse: 1.0180\n","Epoch 19/100\n","2/2 [==============================] - 0s 37ms/step - loss: 0.4320 - mse: 0.4320 - val_loss: 0.6123 - val_mse: 0.6123\n","Epoch 20/100\n","2/2 [==============================] - 0s 45ms/step - loss: 0.6015 - mse: 0.6015 - val_loss: 1.3130 - val_mse: 1.3130\n","Epoch 21/100\n","2/2 [==============================] - 0s 41ms/step - loss: 1.4696 - mse: 1.4696 - val_loss: 1.7066 - val_mse: 1.7066\n","Epoch 22/100\n","2/2 [==============================] - 0s 39ms/step - loss: 1.7316 - mse: 1.7316 - val_loss: 1.3134 - val_mse: 1.3134\n","Epoch 23/100\n","2/2 [==============================] - 0s 37ms/step - loss: 1.1445 - mse: 1.1445 - val_loss: 0.6792 - val_mse: 0.6792\n","Epoch 24/100\n","2/2 [==============================] - 0s 37ms/step - loss: 0.4577 - mse: 0.4577 - val_loss: 0.5976 - val_mse: 0.5976\n","Epoch 25/100\n","2/2 [==============================] - 0s 38ms/step - loss: 0.3647 - mse: 0.3647 - val_loss: 0.9959 - val_mse: 0.9959\n","Epoch 26/100\n","2/2 [==============================] - 0s 39ms/step - loss: 0.6705 - mse: 0.6705 - val_loss: 1.2605 - val_mse: 1.2605\n","Epoch 27/100\n","2/2 [==============================] - 0s 41ms/step - loss: 0.8082 - mse: 0.8082 - val_loss: 1.0866 - val_mse: 1.0866\n","Epoch 28/100\n","2/2 [==============================] - 0s 39ms/step - loss: 0.5914 - mse: 0.5914 - val_loss: 0.7303 - val_mse: 0.7303\n","Epoch 29/100\n","2/2 [==============================] - 0s 40ms/step - loss: 0.3545 - mse: 0.3545 - val_loss: 0.5446 - val_mse: 0.5446\n","Epoch 30/100\n","2/2 [==============================] - 0s 38ms/step - loss: 0.2949 - mse: 0.2949 - val_loss: 0.5819 - val_mse: 0.5819\n","Epoch 31/100\n","2/2 [==============================] - 0s 41ms/step - loss: 0.4279 - mse: 0.4279 - val_loss: 0.6354 - val_mse: 0.6354\n","Epoch 32/100\n","2/2 [==============================] - 0s 42ms/step - loss: 0.4733 - mse: 0.4733 - val_loss: 0.5891 - val_mse: 0.5891\n","Epoch 33/100\n","2/2 [==============================] - 0s 38ms/step - loss: 0.3783 - mse: 0.3783 - val_loss: 0.5379 - val_mse: 0.5379\n","Epoch 34/100\n","2/2 [==============================] - 0s 36ms/step - loss: 0.2879 - mse: 0.2879 - val_loss: 0.5710 - val_mse: 0.5710\n","Epoch 35/100\n","2/2 [==============================] - 0s 36ms/step - loss: 0.2926 - mse: 0.2926 - val_loss: 0.6556 - val_mse: 0.6556\n","Epoch 36/100\n","2/2 [==============================] - 0s 38ms/step - loss: 0.3400 - mse: 0.3400 - val_loss: 0.6841 - val_mse: 0.6841\n","Epoch 37/100\n","2/2 [==============================] - 0s 38ms/step - loss: 0.3474 - mse: 0.3474 - val_loss: 0.6372 - val_mse: 0.6372\n","Epoch 38/100\n","2/2 [==============================] - 0s 40ms/step - loss: 0.3165 - mse: 0.3165 - val_loss: 0.5602 - val_mse: 0.5602\n","Epoch 39/100\n","2/2 [==============================] - 0s 38ms/step - loss: 0.2824 - mse: 0.2824 - val_loss: 0.5321 - val_mse: 0.5321\n","Epoch 40/100\n","2/2 [==============================] - 0s 42ms/step - loss: 0.2940 - mse: 0.2940 - val_loss: 0.5322 - val_mse: 0.5322\n","Epoch 41/100\n","2/2 [==============================] - 0s 38ms/step - loss: 0.3033 - mse: 0.3033 - val_loss: 0.5299 - val_mse: 0.5299\n","Epoch 42/100\n","2/2 [==============================] - 0s 38ms/step - loss: 0.2910 - mse: 0.2910 - val_loss: 0.5303 - val_mse: 0.5303\n","Epoch 43/100\n","2/2 [==============================] - 0s 35ms/step - loss: 0.2768 - mse: 0.2768 - val_loss: 0.5486 - val_mse: 0.5486\n","Epoch 44/100\n","2/2 [==============================] - 0s 38ms/step - loss: 0.2722 - mse: 0.2722 - val_loss: 0.5709 - val_mse: 0.5709\n","Epoch 45/100\n","2/2 [==============================] - 0s 52ms/step - loss: 0.2821 - mse: 0.2821 - val_loss: 0.5789 - val_mse: 0.5789\n","Epoch 46/100\n","2/2 [==============================] - 0s 52ms/step - loss: 0.2813 - mse: 0.2813 - val_loss: 0.5620 - val_mse: 0.5620\n","Epoch 47/100\n","2/2 [==============================] - 0s 37ms/step - loss: 0.2716 - mse: 0.2716 - val_loss: 0.5413 - val_mse: 0.5413\n","Epoch 48/100\n","2/2 [==============================] - 0s 42ms/step - loss: 0.2739 - mse: 0.2739 - val_loss: 0.5259 - val_mse: 0.5259\n","Epoch 49/100\n","2/2 [==============================] - 0s 38ms/step - loss: 0.2722 - mse: 0.2722 - val_loss: 0.5225 - val_mse: 0.5225\n","Epoch 50/100\n","2/2 [==============================] - 0s 37ms/step - loss: 0.2719 - mse: 0.2719 - val_loss: 0.5236 - val_mse: 0.5236\n","Epoch 51/100\n","2/2 [==============================] - 0s 40ms/step - loss: 0.2671 - mse: 0.2671 - val_loss: 0.5281 - val_mse: 0.5281\n","Epoch 52/100\n","2/2 [==============================] - 0s 34ms/step - loss: 0.2626 - mse: 0.2626 - val_loss: 0.5385 - val_mse: 0.5385\n","Epoch 53/100\n","2/2 [==============================] - 0s 36ms/step - loss: 0.2628 - mse: 0.2628 - val_loss: 0.5465 - val_mse: 0.5465\n","Epoch 54/100\n","2/2 [==============================] - 0s 35ms/step - loss: 0.2670 - mse: 0.2670 - val_loss: 0.5503 - val_mse: 0.5503\n","Epoch 55/100\n","2/2 [==============================] - 0s 39ms/step - loss: 0.2651 - mse: 0.2651 - val_loss: 0.5414 - val_mse: 0.5414\n","Epoch 56/100\n","2/2 [==============================] - 0s 37ms/step - loss: 0.2599 - mse: 0.2599 - val_loss: 0.5281 - val_mse: 0.5281\n","Epoch 57/100\n","2/2 [==============================] - 0s 34ms/step - loss: 0.2584 - mse: 0.2584 - val_loss: 0.5181 - val_mse: 0.5181\n","Epoch 58/100\n","2/2 [==============================] - 0s 36ms/step - loss: 0.2596 - mse: 0.2596 - val_loss: 0.5159 - val_mse: 0.5159\n","Epoch 59/100\n","2/2 [==============================] - 0s 36ms/step - loss: 0.2591 - mse: 0.2591 - val_loss: 0.5164 - val_mse: 0.5164\n","Epoch 60/100\n","2/2 [==============================] - 0s 36ms/step - loss: 0.2578 - mse: 0.2578 - val_loss: 0.5220 - val_mse: 0.5220\n","Epoch 61/100\n","2/2 [==============================] - 0s 36ms/step - loss: 0.2547 - mse: 0.2547 - val_loss: 0.5268 - val_mse: 0.5268\n","Epoch 62/100\n","2/2 [==============================] - 0s 37ms/step - loss: 0.2540 - mse: 0.2540 - val_loss: 0.5281 - val_mse: 0.5281\n","Epoch 63/100\n","2/2 [==============================] - 0s 39ms/step - loss: 0.2563 - mse: 0.2563 - val_loss: 0.5296 - val_mse: 0.5296\n","Epoch 64/100\n","2/2 [==============================] - 0s 37ms/step - loss: 0.2549 - mse: 0.2549 - val_loss: 0.5177 - val_mse: 0.5177\n","Epoch 65/100\n","2/2 [==============================] - 0s 36ms/step - loss: 0.2534 - mse: 0.2534 - val_loss: 0.5110 - val_mse: 0.5110\n","Epoch 66/100\n","2/2 [==============================] - 0s 36ms/step - loss: 0.2518 - mse: 0.2518 - val_loss: 0.5136 - val_mse: 0.5136\n","Epoch 67/100\n","2/2 [==============================] - 0s 40ms/step - loss: 0.2489 - mse: 0.2489 - val_loss: 0.5155 - val_mse: 0.5155\n","Epoch 68/100\n","2/2 [==============================] - 0s 38ms/step - loss: 0.2477 - mse: 0.2477 - val_loss: 0.5152 - val_mse: 0.5152\n","Epoch 69/100\n","2/2 [==============================] - 0s 38ms/step - loss: 0.2484 - mse: 0.2484 - val_loss: 0.5181 - val_mse: 0.5181\n","Epoch 70/100\n","2/2 [==============================] - 0s 37ms/step - loss: 0.2460 - mse: 0.2460 - val_loss: 0.5144 - val_mse: 0.5144\n","Epoch 71/100\n","2/2 [==============================] - 0s 38ms/step - loss: 0.2447 - mse: 0.2447 - val_loss: 0.5100 - val_mse: 0.5100\n","Epoch 72/100\n","2/2 [==============================] - 0s 38ms/step - loss: 0.2452 - mse: 0.2452 - val_loss: 0.5052 - val_mse: 0.5052\n","Epoch 73/100\n","2/2 [==============================] - 0s 40ms/step - loss: 0.2434 - mse: 0.2434 - val_loss: 0.5049 - val_mse: 0.5049\n","Epoch 74/100\n","2/2 [==============================] - 0s 38ms/step - loss: 0.2432 - mse: 0.2432 - val_loss: 0.5100 - val_mse: 0.5100\n","Epoch 75/100\n","2/2 [==============================] - 0s 40ms/step - loss: 0.2409 - mse: 0.2409 - val_loss: 0.5108 - val_mse: 0.5108\n","Epoch 76/100\n","2/2 [==============================] - 0s 36ms/step - loss: 0.2406 - mse: 0.2406 - val_loss: 0.5071 - val_mse: 0.5071\n","Epoch 77/100\n","2/2 [==============================] - 0s 38ms/step - loss: 0.2390 - mse: 0.2390 - val_loss: 0.5051 - val_mse: 0.5051\n","Epoch 78/100\n","2/2 [==============================] - 0s 36ms/step - loss: 0.2382 - mse: 0.2382 - val_loss: 0.5022 - val_mse: 0.5022\n","Epoch 79/100\n","2/2 [==============================] - 0s 38ms/step - loss: 0.2371 - mse: 0.2371 - val_loss: 0.5008 - val_mse: 0.5008\n","Epoch 80/100\n","2/2 [==============================] - 0s 42ms/step - loss: 0.2362 - mse: 0.2362 - val_loss: 0.5003 - val_mse: 0.5003\n","Epoch 81/100\n","2/2 [==============================] - 0s 39ms/step - loss: 0.2353 - mse: 0.2353 - val_loss: 0.5012 - val_mse: 0.5012\n","Epoch 82/100\n","2/2 [==============================] - 0s 58ms/step - loss: 0.2344 - mse: 0.2344 - val_loss: 0.5004 - val_mse: 0.5004\n","Epoch 83/100\n","2/2 [==============================] - 0s 37ms/step - loss: 0.2370 - mse: 0.2370 - val_loss: 0.5038 - val_mse: 0.5038\n","Epoch 84/100\n","2/2 [==============================] - 0s 40ms/step - loss: 0.2330 - mse: 0.2330 - val_loss: 0.4986 - val_mse: 0.4986\n","Epoch 85/100\n","2/2 [==============================] - 0s 40ms/step - loss: 0.2314 - mse: 0.2314 - val_loss: 0.4936 - val_mse: 0.4936\n","Epoch 86/100\n","2/2 [==============================] - 0s 37ms/step - loss: 0.2307 - mse: 0.2307 - val_loss: 0.4903 - val_mse: 0.4903\n","Epoch 87/100\n","2/2 [==============================] - 0s 37ms/step - loss: 0.2307 - mse: 0.2307 - val_loss: 0.4883 - val_mse: 0.4883\n","Epoch 88/100\n","2/2 [==============================] - 0s 37ms/step - loss: 0.2296 - mse: 0.2296 - val_loss: 0.4899 - val_mse: 0.4899\n","Epoch 89/100\n","2/2 [==============================] - 0s 40ms/step - loss: 0.2291 - mse: 0.2291 - val_loss: 0.4961 - val_mse: 0.4961\n","Epoch 90/100\n","2/2 [==============================] - 0s 38ms/step - loss: 0.2276 - mse: 0.2276 - val_loss: 0.4950 - val_mse: 0.4950\n","Epoch 91/100\n","2/2 [==============================] - 0s 41ms/step - loss: 0.2290 - mse: 0.2290 - val_loss: 0.4967 - val_mse: 0.4967\n","Epoch 92/100\n","2/2 [==============================] - 0s 39ms/step - loss: 0.2259 - mse: 0.2259 - val_loss: 0.4897 - val_mse: 0.4897\n","Epoch 93/100\n","2/2 [==============================] - 0s 38ms/step - loss: 0.2266 - mse: 0.2266 - val_loss: 0.4813 - val_mse: 0.4813\n","Epoch 94/100\n","2/2 [==============================] - 0s 37ms/step - loss: 0.2289 - mse: 0.2289 - val_loss: 0.4780 - val_mse: 0.4780\n","Epoch 95/100\n","2/2 [==============================] - 0s 38ms/step - loss: 0.2250 - mse: 0.2250 - val_loss: 0.4857 - val_mse: 0.4857\n","Epoch 96/100\n","2/2 [==============================] - 0s 39ms/step - loss: 0.2242 - mse: 0.2242 - val_loss: 0.4947 - val_mse: 0.4947\n","Epoch 97/100\n","2/2 [==============================] - 0s 37ms/step - loss: 0.2234 - mse: 0.2234 - val_loss: 0.4891 - val_mse: 0.4891\n","Epoch 98/100\n","2/2 [==============================] - 0s 41ms/step - loss: 0.2209 - mse: 0.2209 - val_loss: 0.4850 - val_mse: 0.4850\n","Epoch 99/100\n","2/2 [==============================] - 0s 39ms/step - loss: 0.2196 - mse: 0.2196 - val_loss: 0.4798 - val_mse: 0.4798\n","Epoch 100/100\n","2/2 [==============================] - 0s 43ms/step - loss: 0.2195 - mse: 0.2195 - val_loss: 0.4754 - val_mse: 0.4754\n","1/1 [==============================] - 0s 30ms/step - loss: 0.2497 - mse: 0.2497\n","Mean Squared Error: 0.24974048137664795\n","1/1 [==============================] - 0s 106ms/step\n","Predictions: [2.0781896 1.3192562 1.6495048 1.4828702 2.0961487 1.4135627 1.3914634\n"," 1.6838125 2.0256672 1.9944943]\n"]}],"source":["num_df=df.drop([\"id\",\"mode\",\"name\", \"Artist\", \"Streams (Billions)\", \"Release Date\"], axis=1)\n","\n","import tensorflow as tf\n","from tensorflow import keras\n","from tensorflow.keras import layers\n","\n","# create a neural network model\n","model = keras.Sequential()\n","model.add(layers.Dense(64, input_shape=(X_train.shape[1],), activation='relu'))\n","model.add(layers.Dense(64, activation='relu'))\n","model.add(layers.Dense(1, activation='linear'))\n","\n","# compile the model\n","model.compile(loss='mse', optimizer='adam', metrics=['mse'])\n","\n","# train the model\n","history = model.fit(X_train, y_train, epochs=100, batch_size=32, validation_split=0.2)\n","\n","# evaluate the model on the test set\n","mse, _ = model.evaluate(X_test, y_test)\n","print(\"Mean Squared Error:\", mse)\n","\n","# make predictions on new data\n","X_new = num_df.iloc[0:10,:]\n","predictions = model.predict(X_new)\n","print(\"Predictions:\", predictions.flatten())\n"]},{"cell_type":"markdown","id":"3f929316","metadata":{"papermill":{"duration":0.132897,"end_time":"2023-03-09T09:44:23.882142","exception":false,"start_time":"2023-03-09T09:44:23.749245","status":"completed"},"tags":[]},"source":["# The End\n","\n","**These are very simple,basic and striaht forward predictions that were created on one of the cleaned data one can find. In real life scenarios it won't be that easy to clean as well as create a model and fine tuning it.**\n","\n","**Hope this was very useful notebook. Hit a upvote if this was useful**"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.12"},"papermill":{"default_parameters":{},"duration":50.049299,"end_time":"2023-03-09T09:44:27.623672","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2023-03-09T09:43:37.574373","version":"2.4.0"}},"nbformat":4,"nbformat_minor":5}