CAD_Phase3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7cfbbf8f-00a6-4d4f-8a23-fd1bc959cfec",
   "metadata": {},
   "source": [
    "# Import Necessary Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8f854d04-e54e-421f-921e-fa0a1ce96297",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import pyodbc\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d29177fd-4ba6-4d81-91a4-46bdff696909",
   "metadata": {},
   "source": [
    "# Establishing Connection to the IBM DB2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9d2e3078-abe2-472d-9edd-ac80e28996a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IBM DB2 is Connected\n"
     ]
    }
   ],
   "source": [
    "con = pyodbc.connect(\"DSN=cadphase3;UID=db2admin;PWD=db2admin\")\n",
    "print(\"IBM DB2 is Connected\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "599976d4-1628-48d3-abae-c5ec004c7d39",
   "metadata": {},
   "outputs": [],
   "source": [
    "cur=con.cursor()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f5fc0c8-46ab-43ca-aca8-b715bfda78b6",
   "metadata": {},
   "source": [
    "# Creating a Table named Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "063f0d27-093a-4c68-b588-49d1dc969d10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pyodbc.Cursor at 0x22165922fb0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.execute(\"Create table Regression(Dept int,Idept int)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cedc721-0b8c-49e3-b17e-5318ce2f494d",
   "metadata": {},
   "source": [
    "# Inserting Values into the Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "060f0d3d-8ce0-4d5d-a88b-bc2c80927c43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pyodbc.Cursor at 0x22165922fb0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.execute(\"insert into Regression values(2,4)\")\n",
    "cur.execute(\"insert into Regression values(3,9)\")\n",
    "cur.execute(\"insert into Regression values(4,16)\")\n",
    "cur.execute(\"insert into Regression values(5,25)\")\n",
    "cur.execute(\"insert into Regression values(6,36)\")\n",
    "cur.execute(\"insert into Regression values(7,49)\")\n",
    "cur.execute(\"insert into Regression values(8,64)\")\n",
    "cur.execute(\"insert into Regression values(9,81)\")\n",
    "cur.execute(\"insert into Regression values(10,100)\")\n",
    "cur.execute(\"insert into Regression values(11,121)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "45592b82-411a-43c2-b6d4-d063b420db14",
   "metadata": {},
   "outputs": [],
   "source": [
    "cur.commit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9bb61027-8110-45a0-9ef8-2587e188a15c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pyodbc.Cursor at 0x22165922fb0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.execute(\"select * from Regression_data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "29d602c2-5ff4-4989-b872-fbcc810b69b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=cur.fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bd91d020-1779-454e-b07c-73bcdbbc45aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=np.array(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f13830c4-864b-4b49-a7c7-26659bebc227",
   "metadata": {},
   "source": [
    "# Exporting the Table Values to the Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "270ffff2-8855-4a6a-be89-4bbb915ddf1c",
   "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>Dept</th>\n",
       "      <th>Idept</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Dept  Idept\n",
       "0      2      4\n",
       "1      3      9\n",
       "2      4     16\n",
       "3      2      4\n",
       "4      3      9\n",
       "5      4     16\n",
       "6      5     25\n",
       "7      6     36\n",
       "8      7     49\n",
       "9      8     64\n",
       "10     9     81\n",
       "11    10    100\n",
       "12    11    121"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.reshape((13,2))\n",
    "df=pd.DataFrame(data,columns=['Dept','Idept'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c86f83d8-9f27-4e4d-b61b-7c7a49093985",
   "metadata": {},
   "source": [
    "# Creating Machine learing model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "db272d49-c757-4666-b31a-4a2788a0829c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "RandomForestRegressor(max_depth=2)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg=RandomForestRegressor(max_depth=2)\n",
    "reg.fit(df[['Dept']],df.Idept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7a36bd33-7cb4-41f6-ab78-894f73386c35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11.35807937])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[4]])"
   ]
  }
 ],
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