Assignment 10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "afc6ca90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Unnamed: 0  album_release_year  danceability  energy  key  loudness  \\\n",
      "0             1                2021         0.684   0.677    7    -7.355   \n",
      "1             2                2021         0.700   0.664    7    -8.211   \n",
      "2             3                2021         0.588   0.856    9    -5.242   \n",
      "3             4                2021         0.785   0.623   11    -6.923   \n",
      "4             5                2021         0.687   0.890    8    -5.990   \n",
      "..          ...                 ...           ...     ...  ...       ...   \n",
      "125         126                2009         0.550   0.625    1    -8.213   \n",
      "126         127                2009         0.618   0.732    5    -4.609   \n",
      "127         128                2009         0.567   0.572    9    -7.694   \n",
      "128         129                2009         0.363   0.135    7   -11.166   \n",
      "129         130                2009         0.753   0.421    7    -6.988   \n",
      "\n",
      "     mode  speechiness  acousticness  instrumentalness  liveness  valence  \\\n",
      "0       1       0.0401      0.110000          0.009750    0.3400    0.370   \n",
      "1       1       0.0394      0.306000          0.000019    0.2490    0.585   \n",
      "2       0       0.0316      0.000818          0.073900    0.2380    0.635   \n",
      "3       1       0.0403      0.050500          0.125000    0.0539    0.711   \n",
      "4       1       0.0344      0.003960          0.000939    0.0436    0.854   \n",
      "..    ...          ...           ...               ...       ...      ...   \n",
      "125     0       0.0399      0.149000          0.000000    0.1610    0.317   \n",
      "126     1       0.0724      0.147000          0.000009    0.0925    0.444   \n",
      "127     1       0.0318      0.166000          0.000000    0.1030    0.205   \n",
      "128     0       0.0295      0.976000          0.000265    0.1070    0.195   \n",
      "129     1       0.0551      0.103000          0.000040    0.1390    0.134   \n",
      "\n",
      "       tempo  time_signature  disc_number  duration_ms  explicit  is_local  \\\n",
      "0    142.048               4            1       204400     False     False   \n",
      "1    142.009               4            1       223065     False     False   \n",
      "2     96.966               4            1       175044     False     False   \n",
      "3    100.991               4            1       216758     False     False   \n",
      "4    113.060               4            1       172232     False     False   \n",
      "..       ...             ...          ...          ...       ...       ...   \n",
      "125  149.927               3            1       277013     False     False   \n",
      "126  140.086               4            1       273786     False     False   \n",
      "127  138.062               4            1       285733     False     False   \n",
      "128   98.450               4            1       233000     False     False   \n",
      "129  140.031               4            1       345506     False     False   \n",
      "\n",
      "     track_number  \n",
      "0               1  \n",
      "1               2  \n",
      "2               3  \n",
      "3               4  \n",
      "4               5  \n",
      "..            ...  \n",
      "125            10  \n",
      "126            11  \n",
      "127            12  \n",
      "128            13  \n",
      "129            14  \n",
      "\n",
      "[130 rows x 19 columns]\n"
     ]
    }
   ],
   "source": [
    "#1928152 Animesh Pathak Assignment 10\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn import tree\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (12.0, 9.0)\n",
    "df1 = pd.read_csv('C:\\\\Users\\\\KIIT\\\\Downloads\\\\twentyscience.csv')\n",
    "#Kaggle Dataset https://www.kaggle.com/federicoseijo/twenty-one-pilots-discography/version/1\n",
    "print(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f0cbe06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(130, 19)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isnull().sum()\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "18a195a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2021, 2013, 2013, 2013, 2013, 2015, 2013, 2015, 2013, 2015, 2013,\n",
       "       2021, 2018, 2013, 2021, 2013, 2009, 2013, 2021, 2013, 2013, 2013,\n",
       "       2018, 2013, 2013, 2013], dtype=int64)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df1.drop(\"album_release_year\",axis=1)\n",
    "Y = df1[\"album_release_year\"] \n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.20, random_state=0)\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(X_train,Y_train)\n",
    "Y_pred=model.predict(X_test)\n",
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d38e8958",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy=metrics.accuracy_score(Y_test,Y_pred)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "06e16ed6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  0  0  0  0]\n",
      " [ 0 16  0  0  0]\n",
      " [ 0  0  3  0  0]\n",
      " [ 0  0  0  2  0]\n",
      " [ 0  0  0  0  4]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        2009       1.00      1.00      1.00         1\n",
      "        2013       1.00      1.00      1.00        16\n",
      "        2015       1.00      1.00      1.00         3\n",
      "        2018       1.00      1.00      1.00         2\n",
      "        2021       1.00      1.00      1.00         4\n",
      "\n",
      "    accuracy                           1.00        26\n",
      "   macro avg       1.00      1.00      1.00        26\n",
      "weighted avg       1.00      1.00      1.00        26\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(Y_test, Y_pred))\n",
    "print(classification_report(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "74862c58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.0\n",
      "Mean Squared Error: 0.0\n",
      "Root Mean Squared Error: 0.0\n"
     ]
    }
   ],
   "source": [
    "print('Mean Absolute Error:', metrics.mean_absolute_error(Y_test, Y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(Y_test, Y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(Y_test, Y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9e729075",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(372.0, 428.08500000000004, 'X[0] <= 39.5\\ngini = 0.611\\nsamples = 104\\nvalue = [13, 61, 11, 12, 7]'),\n",
       " Text(223.20000000000002, 305.775, 'X[0] <= 25.0\\ngini = 0.651\\nsamples = 30\\nvalue = [0, 0, 11, 12, 7]'),\n",
       " Text(148.8, 183.46500000000003, 'X[0] <= 11.0\\ngini = 0.465\\nsamples = 19\\nvalue = [0, 0, 0, 12, 7]'),\n",
       " Text(74.4, 61.15499999999997, 'gini = 0.0\\nsamples = 7\\nvalue = [0, 0, 0, 0, 7]'),\n",
       " Text(223.20000000000002, 61.15499999999997, 'gini = 0.0\\nsamples = 12\\nvalue = [0, 0, 0, 12, 0]'),\n",
       " Text(297.6, 183.46500000000003, 'gini = 0.0\\nsamples = 11\\nvalue = [0, 0, 11, 0, 0]'),\n",
       " Text(520.8000000000001, 305.775, 'X[0] <= 116.5\\ngini = 0.29\\nsamples = 74\\nvalue = [13, 61, 0, 0, 0]'),\n",
       " Text(446.40000000000003, 183.46500000000003, 'gini = 0.0\\nsamples = 61\\nvalue = [0, 61, 0, 0, 0]'),\n",
       " Text(595.2, 183.46500000000003, 'gini = 0.0\\nsamples = 13\\nvalue = [13, 0, 0, 0, 0]')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(model,filled = True)"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}