Pickle&sklearnjoblib_libraries.ipynb

{
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   "execution_count": 1,
   "id": "b10076fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "82a153ba",
   "metadata": {
    "scrolled": true
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    {
     "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",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2600</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "      <td>680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "      <td>725000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   area   price\n",
       "0  2600  550000\n",
       "1  3000  565000\n",
       "2  3200  610000\n",
       "3  3600  680000\n",
       "4  4000  725000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"/Users/souravghoshhansda/Library/CloudStorage/OneDrive-UniversityofEssex/MA336/Practice/linear_regression/homeprices.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f5c9552f",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = linear_model.LinearRegression()\n",
    "model.fit(df[['area']],df.price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a6614815",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([135.78767123])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3247e348",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180616.43835616432"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aaf38de0",
   "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([859554.79452055])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[5000]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa6c68a4",
   "metadata": {},
   "source": [
    "Save Model To a File Using Python Pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aed9801e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4d7492ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('model_pickle','wb')as f:\n",
    "    pickle.dump(model,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "11ea1b67",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('model_pickle','rb') as f:\n",
    "    mp=pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a57ac413",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([859554.79452055])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mp.predict([[5000]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2e791ce8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f4c74f8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model_joblib']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(model,'model_joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6d0b6d04",
   "metadata": {},
   "outputs": [],
   "source": [
    "mj=joblib.load('model_joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7121961a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([859554.79452055])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mj.predict([[5000]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7fbe835c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([135.78767123])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mj.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "14607517",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180616.43835616432"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mj.intercept_"
   ]
  }
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