Music Genre Recommendation Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8cc51bd9",
   "metadata": {},
   "source": [
    "# Music Genre Recommendation Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "120bdb77",
   "metadata": {},
   "source": [
    "### We will develop a model to study the effect of (age, gender) input variables on (genre) output variables, which recommends user the genre of music he/she may prefer to listen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bc85ac12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e6a41ef1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Music genre.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d440b073",
   "metadata": {},
   "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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>HipHop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>HipHop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>HipHop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>Jazz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>Jazz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>Jazz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>Dance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>Dance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>Dance</td>\n",
       "    </tr>\n",
       "    <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",
       "</table>\n",
       "</div>"
      ],
      "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7d899fe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting dataset into input and output variables\n",
    "inputv = df.drop(columns = [\"genre\"])\n",
    "output = df[\"genre\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2ab22c09",
   "metadata": {},
   "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>age</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e86c6459",
   "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": [
    "output\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "dcc43e65",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, we split our dataset into testing and training dataset.\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(inputv, output,\n",
    "test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c93572b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying algorithm\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model = DecisionTreeClassifier()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "205b8182",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting model into training dataset\n",
    "reg =model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "87e0d9fe",
   "metadata": {},
   "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>age</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  gender\n",
       "6    31       1\n",
       "3    26       1\n",
       "13   27       0\n",
       "2    25       1\n",
       "14   30       0\n",
       "7    33       1"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5547688d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the values by giving intput\n",
    "predict = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c5ff7994",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Classical', 'HipHop', 'Acoustic', 'HipHop', 'Acoustic',\n",
       "       'Classical'], dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "10b0135d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d2eea08d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(x_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89a04872",
   "metadata": {},
   "source": [
    "#### You can see that the test score and training score is near to one another which shows that the model not underfit and overfit, its is accurate "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}