qsar_fish_toxicity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantitative Structure-Activity Relationship of Fish Toxicity"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project aims to analyze the quantitative structure-activity relationship to predict acute aquatic toxicity towards the fish Pimephales promelas (fathead minnow) on a set of 908 chemicals using K-Nearest Neighbor (kNN).\n",
    "\n",
    "The dataset consists of six molecular descriptor as features and their corresponding LC50 values, which are:\n",
    "\n",
    "| Feature   | Description |\n",
    "| ----------| ------------------------------------------------------------ |\n",
    "| CIC0      | Set of indices of neighbourhood symmetry                     |\n",
    "| SM1_Dz(Z) | Set of descriptors calculated from 2D matrices derived from the molecular graph (2D matrix-based descriptors) |\n",
    "| GATS1i    | 2D Geary autocorrelation descriptor                              |\n",
    "| NdsCH     | count the number of unsaturated sp2 carbon atoms of the type =CH-|\n",
    "| NdssC     | Count the number of unsaturated sp2 carbon atoms of the type =C  |\n",
    "| MLOGP     | The octanol-water partitioning coefficient (log P) calculated by means of the Moriguchi model |\n",
    "\n",
    "\n",
    "Dataset source : https://archive.ics.uci.edu/ml/datasets/QSAR+fish+toxicity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>CIC0</th>\n",
       "      <th>SM1_Dz(Z)</th>\n",
       "      <th>GATS1i</th>\n",
       "      <th>NdsCH</th>\n",
       "      <th>NdssC</th>\n",
       "      <th>MLOGP</th>\n",
       "      <th>LC50 [-LOG(mol/L)]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.260</td>\n",
       "      <td>0.829</td>\n",
       "      <td>1.676</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.453</td>\n",
       "      <td>3.770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.189</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.863</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.348</td>\n",
       "      <td>3.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.125</td>\n",
       "      <td>0.638</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.348</td>\n",
       "      <td>3.531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.027</td>\n",
       "      <td>0.331</td>\n",
       "      <td>1.472</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.807</td>\n",
       "      <td>3.510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.094</td>\n",
       "      <td>0.827</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.886</td>\n",
       "      <td>5.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.222</td>\n",
       "      <td>0.331</td>\n",
       "      <td>2.177</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.706</td>\n",
       "      <td>1.819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.179</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.063</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.942</td>\n",
       "      <td>3.947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.938</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.851</td>\n",
       "      <td>3.513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.620</td>\n",
       "      <td>0.499</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.942</td>\n",
       "      <td>4.402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.834</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.591</td>\n",
       "      <td>3.021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CIC0  SM1_Dz(Z)  GATS1i  NdsCH  NdssC  MLOGP  LC50 [-LOG(mol/L)]\n",
       "0  3.260      0.829   1.676      0      1  1.453               3.770\n",
       "1  2.189      0.580   0.863      0      0  1.348               3.115\n",
       "2  2.125      0.638   0.831      0      0  1.348               3.531\n",
       "3  3.027      0.331   1.472      1      0  1.807               3.510\n",
       "4  2.094      0.827   0.860      0      0  1.886               5.390\n",
       "5  3.222      0.331   2.177      0      0  0.706               1.819\n",
       "6  3.179      0.000   1.063      0      0  2.942               3.947\n",
       "7  3.000      0.000   0.938      1      0  2.851               3.513\n",
       "8  2.620      0.499   0.990      0      0  2.942               4.402\n",
       "9  2.834      0.134   0.950      0      0  1.591               3.021"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"dataset/qsar_fish_toxicity.csv\")\n",
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Summarization and Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shows the summary of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 908 entries, 0 to 907\n",
      "Data columns (total 7 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   CIC0                908 non-null    float64\n",
      " 1   SM1_Dz(Z)           908 non-null    float64\n",
      " 2   GATS1i              908 non-null    float64\n",
      " 3   NdsCH               908 non-null    int64  \n",
      " 4   NdssC               908 non-null    int64  \n",
      " 5   MLOGP               908 non-null    float64\n",
      " 6   LC50 [-LOG(mol/L)]  908 non-null    float64\n",
      "dtypes: float64(5), int64(2)\n",
      "memory usage: 49.8 KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen that there's no missing values in this dataset, and all variables are in numeric values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate the descriptive statistics of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CIC0</th>\n",
       "      <th>SM1_Dz(Z)</th>\n",
       "      <th>GATS1i</th>\n",
       "      <th>NdsCH</th>\n",
       "      <th>NdssC</th>\n",
       "      <th>MLOGP</th>\n",
       "      <th>LC50 [-LOG(mol/L)]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>908.000000</td>\n",
       "      <td>908.000000</td>\n",
       "      <td>908.000000</td>\n",
       "      <td>908.000000</td>\n",
       "      <td>908.000000</td>\n",
       "      <td>908.000000</td>\n",
       "      <td>908.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.898129</td>\n",
       "      <td>0.628468</td>\n",
       "      <td>1.293591</td>\n",
       "      <td>0.229075</td>\n",
       "      <td>0.485683</td>\n",
       "      <td>2.109285</td>\n",
       "      <td>4.064431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.756088</td>\n",
       "      <td>0.428459</td>\n",
       "      <td>0.394303</td>\n",
       "      <td>0.605335</td>\n",
       "      <td>0.861279</td>\n",
       "      <td>1.433181</td>\n",
       "      <td>1.455698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.667000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.396000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.884000</td>\n",
       "      <td>0.053000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.347000</td>\n",
       "      <td>0.223000</td>\n",
       "      <td>0.950750</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.209000</td>\n",
       "      <td>3.151750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.934000</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>1.240500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.127000</td>\n",
       "      <td>3.987500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.407000</td>\n",
       "      <td>0.892750</td>\n",
       "      <td>1.562250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.105000</td>\n",
       "      <td>4.907500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.926000</td>\n",
       "      <td>2.171000</td>\n",
       "      <td>2.920000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.515000</td>\n",
       "      <td>9.612000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CIC0   SM1_Dz(Z)      GATS1i       NdsCH       NdssC       MLOGP  \\\n",
       "count  908.000000  908.000000  908.000000  908.000000  908.000000  908.000000   \n",
       "mean     2.898129    0.628468    1.293591    0.229075    0.485683    2.109285   \n",
       "std      0.756088    0.428459    0.394303    0.605335    0.861279    1.433181   \n",
       "min      0.667000    0.000000    0.396000    0.000000    0.000000   -2.884000   \n",
       "25%      2.347000    0.223000    0.950750    0.000000    0.000000    1.209000   \n",
       "50%      2.934000    0.570000    1.240500    0.000000    0.000000    2.127000   \n",
       "75%      3.407000    0.892750    1.562250    0.000000    1.000000    3.105000   \n",
       "max      5.926000    2.171000    2.920000    4.000000    6.000000    6.515000   \n",
       "\n",
       "       LC50 [-LOG(mol/L)]  \n",
       "count          908.000000  \n",
       "mean             4.064431  \n",
       "std              1.455698  \n",
       "min              0.053000  \n",
       "25%              3.151750  \n",
       "50%              3.987500  \n",
       "75%              4.907500  \n",
       "max              9.612000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the correlation of each columns with the LC50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>CIC0</th>\n",
       "      <th>SM1_Dz(Z)</th>\n",
       "      <th>GATS1i</th>\n",
       "      <th>NdsCH</th>\n",
       "      <th>NdssC</th>\n",
       "      <th>MLOGP</th>\n",
       "      <th>LC50 [-LOG(mol/L)]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CIC0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.235360</td>\n",
       "      <td>0.147622</td>\n",
       "      <td>0.121341</td>\n",
       "      <td>0.246639</td>\n",
       "      <td>0.463867</td>\n",
       "      <td>0.291854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SM1_Dz(Z)</th>\n",
       "      <td>-0.235360</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.145967</td>\n",
       "      <td>-0.141401</td>\n",
       "      <td>0.163179</td>\n",
       "      <td>0.200663</td>\n",
       "      <td>0.410893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GATS1i</th>\n",
       "      <td>0.147622</td>\n",
       "      <td>-0.145967</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010657</td>\n",
       "      <td>0.092410</td>\n",
       "      <td>-0.450739</td>\n",
       "      <td>-0.397965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NdsCH</th>\n",
       "      <td>0.121341</td>\n",
       "      <td>-0.141401</td>\n",
       "      <td>-0.010657</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.188164</td>\n",
       "      <td>0.048620</td>\n",
       "      <td>0.172004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NdssC</th>\n",
       "      <td>0.246639</td>\n",
       "      <td>0.163179</td>\n",
       "      <td>0.092410</td>\n",
       "      <td>0.188164</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.028499</td>\n",
       "      <td>0.172390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLOGP</th>\n",
       "      <td>0.463867</td>\n",
       "      <td>0.200663</td>\n",
       "      <td>-0.450739</td>\n",
       "      <td>0.048620</td>\n",
       "      <td>0.028499</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.651664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LC50 [-LOG(mol/L)]</th>\n",
       "      <td>0.291854</td>\n",
       "      <td>0.410893</td>\n",
       "      <td>-0.397965</td>\n",
       "      <td>0.172004</td>\n",
       "      <td>0.172390</td>\n",
       "      <td>0.651664</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        CIC0  SM1_Dz(Z)    GATS1i     NdsCH     NdssC  \\\n",
       "CIC0                1.000000  -0.235360  0.147622  0.121341  0.246639   \n",
       "SM1_Dz(Z)          -0.235360   1.000000 -0.145967 -0.141401  0.163179   \n",
       "GATS1i              0.147622  -0.145967  1.000000 -0.010657  0.092410   \n",
       "NdsCH               0.121341  -0.141401 -0.010657  1.000000  0.188164   \n",
       "NdssC               0.246639   0.163179  0.092410  0.188164  1.000000   \n",
       "MLOGP               0.463867   0.200663 -0.450739  0.048620  0.028499   \n",
       "LC50 [-LOG(mol/L)]  0.291854   0.410893 -0.397965  0.172004  0.172390   \n",
       "\n",
       "                       MLOGP  LC50 [-LOG(mol/L)]  \n",
       "CIC0                0.463867            0.291854  \n",
       "SM1_Dz(Z)           0.200663            0.410893  \n",
       "GATS1i             -0.450739           -0.397965  \n",
       "NdsCH               0.048620            0.172004  \n",
       "NdssC               0.028499            0.172390  \n",
       "MLOGP               1.000000            0.651664  \n",
       "LC50 [-LOG(mol/L)]  0.651664            1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.corr(method=\"pearson\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the correlation test to the LC50 that has been done above, it is known that MLOGP has the highest correlation of 0.652 and GATS1i has the lowest correlation of -0.398."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the histogram plot of each variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1440 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.hist(figsize=(20, 20), layout=(4, 4));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the target and features in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.drop([\"LC50 [-LOG(mol/L)]\"], axis=1)\n",
    "y = pd.DataFrame(dataset[\"LC50 [-LOG(mol/L)]\"].copy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the dataset into training and testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 42)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the pipeline and select the best k value for the KNN model with cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Squared for k = 2 : 0.500\n",
      "R-Squared for k = 3 : 0.548\n",
      "R-Squared for k = 4 : 0.575\n",
      "R-Squared for k = 5 : 0.588\n",
      "R-Squared for k = 6 : 0.582\n",
      "R-Squared for k = 7 : 0.586\n",
      "R-Squared for k = 8 : 0.591\n",
      "R-Squared for k = 9 : 0.590\n"
     ]
    }
   ],
   "source": [
    "knn_result = []\n",
    "\n",
    "for k in range (2, 10):\n",
    "    clf = Pipeline([\n",
    "        (\"scaler\", scaler),\n",
    "        (\"knn\", KNeighborsRegressor(n_neighbors=k))\n",
    "        ])\n",
    "    clf.fit(X_train, y_train)\n",
    "    cross_val = cross_val_score(clf, X, y, cv=10)\n",
    "    cross_val_avg = cross_val.mean()\n",
    "    knn_result.append(cross_val_avg)\n",
    "    print(f\"R-Squared for k = {k} : {cross_val_avg:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the R-Squared result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "plt.plot([i for i in range(2, 10)], knn_result)\n",
    "plt.xlabel(\"k Neighbors\")\n",
    "plt.ylabel(\"R-Squared Score\")\n",
    "plt.title(\"k-Nearest Neighbors\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen that the R-Squared value decreases with the increase of k used. The best R-Squared is obtained at the k=2, which will then be used to make the KNN model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a KNN model and evaluate the model on the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Squared for the training set : 0.683\n",
      "RMSE for the training set : 0.804\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsRegressor(n_neighbors=8).fit(X_train,y_train)\n",
    "knn_training_pred = knn.predict(X_train)\n",
    "knn_training_rsq = knn.score(X_train, y_train)\n",
    "knn_training_rmse = np.sqrt(mean_squared_error(y_train, knn_training_pred))\n",
    "\n",
    "print(f\"R-Squared for the training set : {knn_training_rsq:.3f}\")\n",
    "print(f\"RMSE for the training set : {knn_training_rmse:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate the model on the testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Squared for the testing set : 0.600\n",
      "RMSE for the testing set : 0.984\n"
     ]
    }
   ],
   "source": [
    "knn_testing_pred = knn.predict(X_test)\n",
    "knn_testing_rsq = knn.score(X_test, y_test)\n",
    "knn_testing_rmse = np.sqrt(mean_squared_error(y_test, knn_testing_pred))\n",
    "\n",
    "print(f\"R-Squared for the testing set : {knn_testing_rsq:.3f}\")\n",
    "print(f\"RMSE for the testing set : {knn_testing_rmse:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the regression plot and the residual plot of the KNN method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "residual_train = y_train - knn_training_pred\n",
    "residual_test = y_test - knn_testing_pred\n",
    "\n",
    "fig=plt.figure(figsize=(20,5))\n",
    "\n",
    "ax1=plt.subplot(1,2,1)\n",
    "ax1.scatter(y_train, knn_training_pred, s=60, alpha=0.8, edgecolor=\"white\", label=\"training\")\n",
    "ax1.scatter(y_test, knn_testing_pred, marker=\"^\", s=60, c=\"r\", alpha=0.8, edgecolor=\"white\", label=\"testing\")\n",
    "ax1.plot([y.min(), y.max()], [y.min(), y.max()], \"k\", lw=1)\n",
    "ax1.set_title(\"Actual vs. Predicted Plot\")\n",
    "ax1.set_xlabel(\"Actual\")\n",
    "ax1.set_ylabel(\"Predicted\")\n",
    "ax1.legend()\n",
    "\n",
    "ax2=plt.subplot(1,2,2)\n",
    "ax2.scatter(y_train, residual_train, s=60, edgecolor=\"white\", alpha=0.8, label=\"training\")\n",
    "ax2.scatter(y_test, residual_test, marker=\"^\", s=60, c=\"r\", edgecolor=\"white\", alpha=0.8, label=\"testing\")\n",
    "ax2.axhline(y=0.3, linewidth= 1, linestyle=\"-\", c=\"black\")\n",
    "ax2.legend()\n",
    "ax2.set_title(\"Residual Plot\")\n",
    "ax2.set_xlabel(\"LC50\")\n",
    "ax2.set_ylabel(\"Residual\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the Final Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model/qsar_fish_toxicity_model.sav']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = \"model/qsar_fish_toxicity_model.sav\"\n",
    "joblib.dump(knn, filename)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "The KNN model to predict acute aquatic toxicity towards the fish Pimephales promelas (fathead minnow) has been successfully developed. Based on our evaluation, the KNN model with k=8 performed relatively good on the training set with an R2 = 0.683 and an RMSE = 0.804. However, on the testing set, the model's performance slightly lower, with an R2 = 0.600 and an RMSE = 0.984.\n",
    "\n",
    "To improve the model's performance, we recommend considering alternative model architectures or adjusting the model's hyperparameters. Additionally, it may be helpful to analyze the characteristics of the training and testing sets to identify any potential issues or biases that could be impacting the model's performance.\n",
    "\n",
    "Overall, the model demonstrated relatively good performance on both the training and testing sets, although further optimization may be necessary to improve its ability to make accurate predictions on unseen examples."
   ]
  }
 ],
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  "interpreter": {
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  "kernelspec": {
   "display_name": "Python 3.8.6 64-bit",
   "language": "python",
   "name": "python3"
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