water_intake_knn_reg.ipynb

{
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   "metadata": {},
   "source": [
    "<b> KNN Regression Model for Water Intake Recommendation </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor\n",
    "from sklearn.metrics import accuracy_score, mean_squared_error\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.inspection import DecisionBoundaryDisplay\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "seed = 0\n",
    "np.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>age</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>activity_level</th>\n",
       "      <th>temperature</th>\n",
       "      <th>water_intake</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>68</td>\n",
       "      <td>1.72</td>\n",
       "      <td>1.5</td>\n",
       "      <td>23</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.6</td>\n",
       "      <td>22</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42</td>\n",
       "      <td>82</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1.8</td>\n",
       "      <td>25</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>90</td>\n",
       "      <td>1.85</td>\n",
       "      <td>1.3</td>\n",
       "      <td>18</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>67</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.5</td>\n",
       "      <td>21</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  weight  height  activity_level  temperature  water_intake\n",
       "0   28      68    1.72             1.5           23           2.7\n",
       "1   35      75    1.78             1.6           22           3.1\n",
       "2   42      82    1.81             1.8           25           2.9\n",
       "3   50      90    1.85             1.3           18           2.3\n",
       "4   29      67    1.69             1.5           21           2.6"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intake_data = pd.read_csv(\"water_intake_datasets/water_drinking_data.csv\")\n",
    "intake_data.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Activity_level measures in METs: \n",
    "https://en.wikipedia.org/wiki/Metabolic_equivalent_of_task"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create train/test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "intake_X = intake_data.iloc[:, :-1]  # features of intake data\n",
    "intake_y = intake_data['water_intake']  # labels of intake data\n",
    "\n",
    "# scale my data\n",
    "scaler = StandardScaler()\n",
    "intake_X_scaled = scaler.fit_transform(intake_X)\n",
    "\n",
    "# create 80%/20% train/test split\n",
    "intake_X_tr, intake_X_te, intake_y_tr, intake_y_te = train_test_split(intake_X_scaled, intake_y, test_size=0.2, random_state=seed)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the best k value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best k value is: 3\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsRegressor()\n",
    "param_grid = {'n_neighbors': [3, 5, 7, 9, 11]}\n",
    "grid_search = GridSearchCV(knn, param_grid, cv=3)\n",
    "grid_search.fit(intake_X_tr, intake_y_tr)\n",
    "best_k = grid_search.best_params_['n_neighbors']\n",
    "print(\"The best k value is: {}\".format(best_k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "KNeighborsRegressor(n_neighbors=3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# initialize KNN reg model\n",
    "model = KNeighborsRegressor(n_neighbors=best_k)\n",
    "model.fit(intake_X_tr, intake_y_tr)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse is 0.013950617283950627\n"
     ]
    }
   ],
   "source": [
    "y_te_pred = model.predict(intake_X_te)\n",
    "mse = mean_squared_error(intake_y_te, y_te_pred)\n",
    "print(\"mse is {}\".format(mse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommend_pred(age, weight, height, activity_level, temperature, model):\n",
    "    X_df = pd.DataFrame({\"age\": [age], \"weight\": [weight], \"height\": [height], \"activity_level\": [activity_level], \"temperature\": [temperature]})\n",
    "    X_scaled = scaler.transform(X_df)\n",
    "    intake_level = model.predict(X_scaled)\n",
    "    return float(intake_level)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example Data Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.7000000000000006\n",
      "3.233333333333333\n",
      "2.6999999999999997\n",
      "2.766666666666667\n",
      "2.8333333333333335\n",
      "2.8333333333333335\n"
     ]
    }
   ],
   "source": [
    "# result in liters\n",
    "print(recommend_pred(25, 60, 1.65, 1.3, 22, model))\n",
    "print(recommend_pred(35, 75, 1.78, 1.6, 24, model))\n",
    "print(recommend_pred(21, 10, 1.85, 1.1, 14, model))\n",
    "print(recommend_pred(21, 10, 1.85, 1.1, 33, model))\n",
    "print(recommend_pred(21, 10, 1.99, 1.1, 33, model))\n",
    "print(recommend_pred(29, 10, 1.99, 1.1, 33, model))"
   ]
  }
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