Multiple Linear - Startup.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import sklearn\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ac216773",
   "metadata": {},
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     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   R&D Spend  Administration  Marketing Spend       State     Profit\n",
       "0  165349.20       136897.80        471784.10    New York  192261.83\n",
       "1  162597.70       151377.59        443898.53  California  191792.06\n",
       "2  153441.51       101145.55        407934.54     Florida  191050.39\n",
       "3  144372.41       118671.85        383199.62    New York  182901.99\n",
       "4  142107.34        91391.77        366168.42     Florida  166187.94"
      ]
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "startup = pd.read_csv(\"C:/Users/user/Desktop/Sneha's Program/ML Datasets/Startup.csv\")\n",
    "startup.head()"
   ]
  },
  {
   "cell_type": "code",
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   "id": "1ea34b8a",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   R&D Spend        50 non-null     float64\n",
      " 1   Administration   50 non-null     float64\n",
      " 2   Marketing Spend  50 non-null     float64\n",
      " 3   State            50 non-null     object \n",
      " 4   Profit           50 non-null     float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 2.1+ KB\n"
     ]
    }
   ],
   "source": [
    "startup.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b3fc5c12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 5)"
      ]
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     "execution_count": 12,
     "metadata": {},
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   ],
   "source": [
    "startup.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "754ce6c1",
   "metadata": {},
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       "array([2, 0, 1, 2, 1, 2, 0, 1, 2, 0, 1, 0, 1, 0, 1, 2, 0, 2, 1, 2, 0, 2,\n",
       "       1, 1, 2, 0, 1, 2, 1, 2, 1, 2, 0, 1, 0, 2, 1, 0, 2, 0, 0, 1, 0, 2,\n",
       "       0, 2, 1, 0, 2, 0])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "df = le.fit_transform(startup['State'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7497cf21",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   R&D Spend  Administration  Marketing Spend       State\n",
       "0  165349.20       136897.80        471784.10    New York\n",
       "1  162597.70       151377.59        443898.53  California\n",
       "2  153441.51       101145.55        407934.54     Florida\n",
       "3  144372.41       118671.85        383199.62    New York\n",
       "4  142107.34        91391.77        366168.42     Florida"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = startup.drop(['Profit'],axis =1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "96cbedd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    192261.83\n",
       "1    191792.06\n",
       "2    191050.39\n",
       "3    182901.99\n",
       "4    166187.94\n",
       "Name: Profit, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = startup['Profit']\n",
    "Y.head()"
   ]
  },
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