Music genre prediction.ipynb

{
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>genre</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
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       "      <td>HipHop</td>\n",
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       "    <tr>\n",
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       "      <td>Jazz</td>\n",
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       "      <th>6</th>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>Classical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>Classical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>Classical</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>Dance</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>Dance</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>25</td>\n",
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       "      <td>Dance</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>Acoustic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>Acoustic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>Acoustic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>Classical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>Classical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Classical</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "    age  gender      genre\n",
       "0    20       1     HipHop\n",
       "1    23       1     HipHop\n",
       "2    25       1     HipHop\n",
       "3    26       1       Jazz\n",
       "4    29       1       Jazz\n",
       "5    30       1       Jazz\n",
       "6    31       1  Classical\n",
       "7    33       1  Classical\n",
       "8    37       1  Classical\n",
       "9    20       0      Dance\n",
       "10   21       0      Dance\n",
       "11   25       0      Dance\n",
       "12   26       0   Acoustic\n",
       "13   27       0   Acoustic\n",
       "14   30       0   Acoustic\n",
       "15   31       0  Classical\n",
       "16   34       0  Classical\n",
       "17   35       0  Classical"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This notebook contains code to predict the type of music a person will like based on age and sex of the person\n",
    "#import the pandas library\n",
    "import pandas as pd\n",
    "music_data = pd.read_csv('music.csv')\n",
    "music_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The next step is to clean the data, however, this dataset is clean. There are no duplicates or null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "    age  gender\n",
       "0    20       1\n",
       "1    23       1\n",
       "2    25       1\n",
       "3    26       1\n",
       "4    29       1\n",
       "5    30       1\n",
       "6    31       1\n",
       "7    33       1\n",
       "8    37       1\n",
       "9    20       0\n",
       "10   21       0\n",
       "11   25       0\n",
       "12   26       0\n",
       "13   27       0\n",
       "14   30       0\n",
       "15   31       0\n",
       "16   34       0\n",
       "17   35       0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the dataset into input and output sets.\n",
    "# The age and gender make up the input set while the genre makes up the output set. \n",
    "# The output set contains predictions that have already been made. Let input set be X and output set y\n",
    "X = music_data.drop(columns = ['genre'])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        HipHop\n",
       "1        HipHop\n",
       "2        HipHop\n",
       "3          Jazz\n",
       "4          Jazz\n",
       "5          Jazz\n",
       "6     Classical\n",
       "7     Classical\n",
       "8     Classical\n",
       "9         Dance\n",
       "10        Dance\n",
       "11        Dance\n",
       "12     Acoustic\n",
       "13     Acoustic\n",
       "14     Acoustic\n",
       "15    Classical\n",
       "16    Classical\n",
       "17    Classical\n",
       "Name: genre, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = music_data['genre']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build a model and train the model\n",
    "#The algorithm we will be using is decision tree, this algorithm comes with scikit learn\n",
    "# import the algotithm from scikit learn and create an instance of the class\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "#instance of the class DecisionTreeClassifier from the tree module in sklearn package\n",
    "model = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train the data using the input and output set\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['HipHop'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict the kind of music a person would like using the age and gender.\n",
    "#model.predict takes in a multidemnsional array, you can pass in as many input to be predicted as wanted\n",
    "# NB: 21 = age, 1 = gender\n",
    "model.predict([ [21, 1] ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Dance'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#more predictions\n",
    "model.predict([ [22, 0] ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#determine accuracy of model\n",
    "# to do this, split the data into test and training sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "# The test size determines the percentage of data to use for training and testing, in this case, we use 80% \n",
    "# for training and 20 % for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can now train using the training set only\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To determine the accuracy we need to compare the output of the test data with the training data\n",
    "from sklearn.metrics import accuracy_score\n",
    "score = accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['music-recommender.joblib']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#persist the model\n",
    "from sklearn.externals import joblib\n",
    "\n",
    "joblib.dump(model, 'music-recommender.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Dance'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To use this file in a program\n",
    "this_model = joblib.load('music-recommender.joblib')\n",
    "new_prediction = this_model.predict([[22,0]])\n",
    "new_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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