glass_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>RI</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>Al</th>\n",
       "      <th>Si</th>\n",
       "      <th>K</th>\n",
       "      <th>Ca</th>\n",
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       "      <th>Fe</th>\n",
       "      <th>Type</th>\n",
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       "      <td>1</td>\n",
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       "      <th>209</th>\n",
       "      <td>1.51623</td>\n",
       "      <td>14.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.88</td>\n",
       "      <td>72.61</td>\n",
       "      <td>0.08</td>\n",
       "      <td>9.18</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
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       "      <td>8.40</td>\n",
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       "      <td>7</td>\n",
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       "      <td>1.94</td>\n",
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      ],
      "text/plain": [
       "          RI     Na    Mg    Al     Si     K    Ca    Ba   Fe  Type\n",
       "0    1.52101  13.64  4.49  1.10  71.78  0.06  8.75  0.00  0.0     1\n",
       "1    1.51761  13.89  3.60  1.36  72.73  0.48  7.83  0.00  0.0     1\n",
       "2    1.51618  13.53  3.55  1.54  72.99  0.39  7.78  0.00  0.0     1\n",
       "3    1.51766  13.21  3.69  1.29  72.61  0.57  8.22  0.00  0.0     1\n",
       "4    1.51742  13.27  3.62  1.24  73.08  0.55  8.07  0.00  0.0     1\n",
       "..       ...    ...   ...   ...    ...   ...   ...   ...  ...   ...\n",
       "209  1.51623  14.14  0.00  2.88  72.61  0.08  9.18  1.06  0.0     7\n",
       "210  1.51685  14.92  0.00  1.99  73.06  0.00  8.40  1.59  0.0     7\n",
       "211  1.52065  14.36  0.00  2.02  73.42  0.00  8.44  1.64  0.0     7\n",
       "212  1.51651  14.38  0.00  1.94  73.61  0.00  8.48  1.57  0.0     7\n",
       "213  1.51711  14.23  0.00  2.08  73.36  0.00  8.62  1.67  0.0     7\n",
       "\n",
       "[214 rows x 10 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"glass.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.iloc[:,0:9]\n",
    "y=df.iloc[:,[9]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "          RI     Na    Mg    Al     Si     K    Ca    Ba   Fe\n",
       "0    1.52101  13.64  4.49  1.10  71.78  0.06  8.75  0.00  0.0\n",
       "1    1.51761  13.89  3.60  1.36  72.73  0.48  7.83  0.00  0.0\n",
       "2    1.51618  13.53  3.55  1.54  72.99  0.39  7.78  0.00  0.0\n",
       "3    1.51766  13.21  3.69  1.29  72.61  0.57  8.22  0.00  0.0\n",
       "4    1.51742  13.27  3.62  1.24  73.08  0.55  8.07  0.00  0.0\n",
       "..       ...    ...   ...   ...    ...   ...   ...   ...  ...\n",
       "209  1.51623  14.14  0.00  2.88  72.61  0.08  9.18  1.06  0.0\n",
       "210  1.51685  14.92  0.00  1.99  73.06  0.00  8.40  1.59  0.0\n",
       "211  1.52065  14.36  0.00  2.02  73.42  0.00  8.44  1.64  0.0\n",
       "212  1.51651  14.38  0.00  1.94  73.61  0.00  8.48  1.57  0.0\n",
       "213  1.51711  14.23  0.00  2.08  73.36  0.00  8.62  1.67  0.0\n",
       "\n",
       "[214 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>209</th>\n",
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       "    <tr>\n",
       "      <th>210</th>\n",
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       "      <th>211</th>\n",
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       "      <th>212</th>\n",
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       "      <th>213</th>\n",
       "      <td>7</td>\n",
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       "<p>214 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Type\n",
       "0       1\n",
       "1       1\n",
       "2       1\n",
       "3       1\n",
       "4       1\n",
       "..    ...\n",
       "209     7\n",
       "210     7\n",
       "211     7\n",
       "212     7\n",
       "213     7\n",
       "\n",
       "[214 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(171, 9)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(171, 1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43, 9)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "std_sc=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>RI</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>Al</th>\n",
       "      <th>Si</th>\n",
       "      <th>K</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Ba</th>\n",
       "      <th>Fe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1.51590</td>\n",
       "      <td>12.82</td>\n",
       "      <td>3.52</td>\n",
       "      <td>1.90</td>\n",
       "      <td>72.86</td>\n",
       "      <td>0.69</td>\n",
       "      <td>7.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>1.51934</td>\n",
       "      <td>13.64</td>\n",
       "      <td>3.54</td>\n",
       "      <td>0.75</td>\n",
       "      <td>72.65</td>\n",
       "      <td>0.16</td>\n",
       "      <td>8.89</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>1.51818</td>\n",
       "      <td>13.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.56</td>\n",
       "      <td>74.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1.52081</td>\n",
       "      <td>13.78</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1.43</td>\n",
       "      <td>71.99</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.85</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1.51860</td>\n",
       "      <td>13.36</td>\n",
       "      <td>3.43</td>\n",
       "      <td>1.43</td>\n",
       "      <td>72.26</td>\n",
       "      <td>0.51</td>\n",
       "      <td>8.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1.53125</td>\n",
       "      <td>10.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>69.81</td>\n",
       "      <td>0.58</td>\n",
       "      <td>13.30</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.51763</td>\n",
       "      <td>12.61</td>\n",
       "      <td>3.59</td>\n",
       "      <td>1.31</td>\n",
       "      <td>73.29</td>\n",
       "      <td>0.58</td>\n",
       "      <td>8.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>1.51588</td>\n",
       "      <td>13.12</td>\n",
       "      <td>3.41</td>\n",
       "      <td>1.58</td>\n",
       "      <td>73.26</td>\n",
       "      <td>0.07</td>\n",
       "      <td>8.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>1.51852</td>\n",
       "      <td>14.09</td>\n",
       "      <td>2.19</td>\n",
       "      <td>1.66</td>\n",
       "      <td>72.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1.51820</td>\n",
       "      <td>12.62</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.83</td>\n",
       "      <td>73.81</td>\n",
       "      <td>0.35</td>\n",
       "      <td>9.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>171 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          RI     Na    Mg    Al     Si     K     Ca    Ba    Fe\n",
       "79   1.51590  12.82  3.52  1.90  72.86  0.69   7.97  0.00  0.00\n",
       "161  1.51934  13.64  3.54  0.75  72.65  0.16   8.89  0.15  0.24\n",
       "109  1.51818  13.72  0.00  0.56  74.45  0.00  10.99  0.00  0.00\n",
       "127  1.52081  13.78  2.28  1.43  71.99  0.49   9.85  0.00  0.17\n",
       "95   1.51860  13.36  3.43  1.43  72.26  0.51   8.60  0.00  0.00\n",
       "..       ...    ...   ...   ...    ...   ...    ...   ...   ...\n",
       "106  1.53125  10.73  0.00  2.10  69.81  0.58  13.30  3.15  0.28\n",
       "14   1.51763  12.61  3.59  1.31  73.29  0.58   8.50  0.00  0.00\n",
       "92   1.51588  13.12  3.41  1.58  73.26  0.07   8.39  0.00  0.19\n",
       "179  1.51852  14.09  2.19  1.66  72.67  0.00   9.32  0.00  0.00\n",
       "102  1.51820  12.62  2.76  0.83  73.81  0.35   9.42  0.00  0.20\n",
       "\n",
       "[171 rows x 9 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=std_sc.fit_transform(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test=std_sc.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.84623746, -0.74467528,  0.52636164, ..., -0.68807645,\n",
       "        -0.33761134, -0.60110996],\n",
       "       [ 0.28077633,  0.33413832,  0.54088841, ..., -0.03692037,\n",
       "        -0.04564469,  1.78472688],\n",
       "       [-0.0992632 ,  0.43938842, -2.03034913, ...,  1.44941417,\n",
       "        -0.33761134, -0.60110996],\n",
       "       ...,\n",
       "       [-0.85278986, -0.34998738,  0.44646443, ..., -0.39080954,\n",
       "        -0.33761134,  1.28767753],\n",
       "       [ 0.0121277 ,  0.92617017, -0.43966828, ...,  0.26742432,\n",
       "        -0.33761134, -0.60110996],\n",
       "       [-0.09271079, -1.00780055, -0.02565546, ...,  0.33820216,\n",
       "        -0.33761134,  1.3870874 ]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=RandomForestClassifier(criterion=\"entropy\",n_estimators=300,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:1: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', n_estimators=300, random_state=42)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43,)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6046511627906976"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model.sav']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename=\"model.sav\"\n",
    "joblib.dump(model,filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.9"
  }
 },
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}