getting data ready-checkpoint.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a472438a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6c828970",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>35431</td>\n",
       "      <td>4</td>\n",
       "      <td>15323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>192714</td>\n",
       "      <td>5</td>\n",
       "      <td>19943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>84714</td>\n",
       "      <td>4</td>\n",
       "      <td>28343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>154365</td>\n",
       "      <td>4</td>\n",
       "      <td>13434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>181577</td>\n",
       "      <td>3</td>\n",
       "      <td>14043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors  Price\n",
       "0   Honda  White          35431      4  15323\n",
       "1     BMW   Blue         192714      5  19943\n",
       "2   Honda  White          84714      4  28343\n",
       "3  Toyota  White         154365      4  13434\n",
       "4  Nissan   Blue         181577      3  14043"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales = pd.read_csv(\"car-sales-extended.csv\")\n",
    "car_sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cbcc0c64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(car_sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02a37bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting data into X and y\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = car_sales.drop(\"Price\", axis=1)\n",
    "y = car_sales[\"Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "158cca89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000e+00, 1.00000e+00, 0.00000e+00, ..., 1.00000e+00,\n",
       "        0.00000e+00, 3.54310e+04],\n",
       "       [1.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "        1.00000e+00, 1.92714e+05],\n",
       "       [0.00000e+00, 1.00000e+00, 0.00000e+00, ..., 1.00000e+00,\n",
       "        0.00000e+00, 8.47140e+04],\n",
       "       ...,\n",
       "       [0.00000e+00, 0.00000e+00, 1.00000e+00, ..., 1.00000e+00,\n",
       "        0.00000e+00, 6.66040e+04],\n",
       "       [0.00000e+00, 1.00000e+00, 0.00000e+00, ..., 1.00000e+00,\n",
       "        0.00000e+00, 2.15883e+05],\n",
       "       [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 1.00000e+00,\n",
       "        0.00000e+00, 2.48360e+05]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# turning categories into numbers\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "categorical_features = [\"Make\",\"Colour\",\"Doors\"]\n",
    "\n",
    "one_hot = OneHotEncoder()\n",
    "\n",
    "transformer = ColumnTransformer([(\"one_hot\",one_hot,categorical_features)],remainder=\"passthrough\")\n",
    "\n",
    "transformed_X = transformer.fit_transform(X)\n",
    "\n",
    "transformed_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9cf913e6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "<p>1000 rows × 13 columns</p>\n",
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      "text/plain": [
       "      0    1    2    3    4    5    6    7    8    9    10   11        12\n",
       "0    0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0   35431.0\n",
       "1    1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  192714.0\n",
       "2    0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0   84714.0\n",
       "3    0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0  154365.0\n",
       "4    0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  181577.0\n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...       ...\n",
       "995  0.0  0.0  0.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0   35820.0\n",
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       "\n",
       "[1000 rows x 13 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(transformed_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f1060f3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating machine learning model\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "model = RandomForestRegressor()\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(transformed_X, y, test_size=0.2)\n",
    "\n",
    "model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b7f22127",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3235867221569877"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  }
 ],
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